Old Calls

The focus of the PhD grant will be on the extraction of information from clinical documents in a multilingual perspective, with a specific emphasis on languages with limited resources.

In the last decade there has been a growing interest for virtual coaching interventions delivered by means of mobile applications and personal assistants to support self-care of patients, including those coping with cancer. Despite the fact that the validity of virtual coaching treatments has been proved repeatedly by previous research, the design of effective behavioral intervention technologies for patient care and well-being remains a challenge.  The aim of the PhD project is to investigate key features of smart coaching solutions for patient care and well-being interventions that are engaging to use by patients and produce effective outcomes from a clinical perspective. The ideal candidate will be strongly motivated in developing design skills in the field of behavioral intervention technologies and conversational agents for applications in healthcare. The PhD position is offered in co-tutoring between the DHLab unit of FBK and the Department of Psychology of the University of Padova.

The focus of the PhD grant will be on the extraction of information from clinical documents in a multilingual perspective, with a specific emphasis on languages with limited resources.

The research theme of this doctoral position will concern automated learning and inference by means of hybrid AI models, including their application to the interpretation of multimodal data.

Automated Planning is the problem of synthesizing courses of actions guaranteed to achieve a desired objective, given a formal model of the system being controlled. A class of problems particularly interesting for applications is temporal planning (also called planning and scheduling) where the discrete decisions of "what to do" are coupled with the problem of scheduling (deciding "when to do"). Planning and scheduling techniques are important in several application domains such as flexible manufacturing and robotics. Unfortunately, these techniques suffer from scalability issues and are often unable to cope with the complexity of real-word scenarios, despite the significant advances in the field.

Recently, efforts such as Deepmind AlphaZero and OpenAI Five hit the headlines, with groundbreaking advancements in the field of reinforcement learning. These techniques are able to automatically learn policies to decide what to do in order to achieve a desired goal. However, they offer no formal guarantee and are not model-based. The research objective of this PhD scholarship is to investigate techniques that combine the formal guarantees offered by automated planning and scheduling with the performance and self-improving capabilities offered by recent advances in deep reinforcement learning to construct adaptive planners that can learn strategies capable of solving problems in a specific application scenario and improve their performance (in terms of both speed and quality) over time.

AI components are more and more used in safety-critical systems in different application domains such as automotive or space. In particular, the increased availability of sensor data gives the opportunity to increase the autonomy these systems with advanced perception, optimized control, and efficient fault detection and recovery. The validation, verification, and safety assurance of AI components in these systems are therefore of paramount importance. However, the uncertainty of Machine Learning (ML) algorithms poses hard challenges for traditional approaches. In this PhD project, we aim at investigating new model-based design techniques to ensure the safe usage of AI/ML components. We will explore the definition of new formal models to represent the uncertainty of the ML models and the related errors, as well as formal verification techniques for the evaluation of the reliability of the system with AI components, and will design and evaluate architectural schemas in specific application scenarios.

While the task of automatically recognising hateful content online has been extensively explored in the last years within the NLP community, what is the best strategy to respond to such messages has only recently entered the research agenda. One of the main issues related to this task is indeed how to best measure the effects of computer generated counter-narratives (i.e. textual responses to hate messages), in order to identify the most promising approaches. This thesis will explore this topic across NLP, NLG and complex networks in order to combine content-based, emotion-based and network-based metrics and apply them effectively to fight online hate via analysis of Social Media content spreading.

Machine and deep learning methods are entering also the industrial sector to automatise 3D monitoring and analysis tasks. The research should investigate the use of AI-based methods to boost photogrammetric 3D inspections for industrial quality control operations. Innovative and advanced AI-based solutions should be developed in order to inspect non-collaborative surfaces (reflective, transparent, etc.) and derive precise 3D results useful for quality control.

Machine learning and deep neural networks are extensively and successfully used to process multimodal data (e.g., audio, video, environmental data) on powerful computers. At the same time, several challenges still need to be solved to bring AI on low consumption devices (e.g., end nodes in an IoT) with limited resources. Recently TinyML approaches are emerging to distribute the intelligence at the far edge in the edge-to-cloud continuum. Exciting research scenarios emerge, spanning from novel, innovative hardware for always-on and event-based sensing to tiny deep learning solutions for inference on resource-constrained platforms based on distillation, quantization, or neural architecture search. The complexity grows if we want to move learning to the edge. Motivated by these scenarios, the research aims to (i) define novel hardware/software approaches to optimize AI at the very edge on energy-efficient embedded devices, in particular for audio processing and/or computer vision; (ii) to explore the potential of distributing and fuse the intelligence in heterogeneous nodes of an IoT (iii) to demonstrate the advantages of the investigated approaches in real-world application scenarios, such as those of smart cities.

Data is often collected at the edges of the network but processed centrally fueled by the availability of computing power provided by the cloud. However, the edge of wireless networks can play a role as a distributed platform for ML mitigating the latency and privacy concerns as well as alleviating backhaul network from the transmission of data to the cloud.

The main goal of this PhD is to investigate the impact of bringing learning at the edges of wireless networks, considering an edge-cloud network which is AI aware and where machine learning algorithms interact with the physical limitations of the wireless medium.

The ability of modeling, understanding and predicting human behaviors, mobility routines and social interactions is fundamental for computational social science and has a range of relevant applications for individuals, companies, and societies at large. In this project, the goal is merging approaches from machine learning and network science (e.g., graph neural networks, multi-agent deep reinforcement learning, etc.) and using data on mobility routines (e.g., GPS and other mobile phone data), face-to-face interactions and communication data in order to develop methods for quantify daily habits, individual dispositions and traits, and behavioral changes. A special attention will be given to the changes on daily human behaviors due to the emergence and spread of the Covid-19 pandemic and other shocks. The Ph.D. project will be conducted within the FBK MobS research unit but with collaborations with several international groups (i.e., MIT Connection Science) and with the ELLIS program of the Human-Centric Machine Learning.

The European open data policies have led to the definition of the concept of data space: ecosystem of data within a specific application domain and based on shared policies and rules where users are enabled to access data in a safe, transparent, reliable way, easy and unified.
In this project, the goal is to provide Human-Centered AI tools capable of enabling a data space for mobility , in the context of the European green deal, keeping a balance between users' freedom and companies' constraints.

In the context of the green deal transition and climate change we are looking for candidates willing to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in long and dense timeseries of remote sensing images.

The candidate will be requested to deal with multi-/hyper-spectral images acquired by passive satellite sensors and/or Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. Among the others, data from ESA Copernicus (Sentinels), ASI PRISMA and COSMO-SkyMed will be considered. The goal is to design novel methods able to use temporal correlation to model landcover behaviors, changes and trends for a better understanding of phenomena over the past and the future for detecting trends and changes for modeling and understanding their impacts on climate and environment.
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:

• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;

• knowledge in pattern recognition, deep learning, image/signal processing, statistic/remote sensing, passive/active sensors.

The need to translate audio input from one language into text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems are now required to be flexible and robust enough to operate in different application scenarios. On one side, the industry calls for key features like real-time processing, domain adaptability, extended language coverage, and the capability to adhere to application-specific constraints. On the other side, society calls for new efforts towards inclusiveness with respect to specific categories and groups (e.g. gender-sensitivity, customization to the needs of impaired users). Both industry and society face the orthogonal challenges posed by the variability of audio conditions (e.g. background noise, strong speakers’ accent, overlapping speakers). The objective of this Ph.D. is to make ST flexible and robust to these and other factors.

Conversational agents are designed to interact with users through various communication channels, such as social media platforms, using natural language. Recently neural end-to-end systems have started to be tested to fight misinformation using argument generation to debunk fake news. Still, Neural Language models suffer from limitations such as hallucination and knowledge lack. Scaling to credible, up-to-date and grounded arguments requires world and domain knowledge together with a deep understanding of argumentative tactics. The goal of this PhD Thesis is to overcome the shortcomings of traditional neural language models, by incorporating several knowledge sources, argumentation and domain features into a constrained generation pipeline.

The research project will focus on the development of tiny machine learning models for learning continuously over time and under domain shift.  The research will focus on developing compact deep learning models (i.e. with reduced memory footprint and computational cost) for domain adaptation and continual learning. Techniques for network pruning and Neural Architecture Search methods will be investigated.

The goal of the thesis is to develop AI models for speech processing which are aware of the computational resources and of the application requirements and are capable of dynamically adapting in order to meet such limitations. This entails not only the search for a trade-off between resources and inference performance but also the possibility to dynamically exploit additional computational resources, eventually expanding the model. The project will address both training and inference phases, starting from state of the art supervised techniques as model compression, neural architecture search, distillation and continual learning and pushing them towards continuous and unsupervised solutions.

Climate change and its impact on countless sectors of society has enormously increased the demand for a comprehensive, robust, timely and reliable climate data analysis that provides support to the adaptation and mitigation policies. Earth System Models (ESMs) that faithfully simulate the cycle of the different components of the Earth System (atmosphere, hydrosphere, cryosphere, biosphere) are the key to address the complex challenges the society is facing, and their development requires expertise at the border between physics and computer science. During the PhD the student will be guided in exploring and applying Artificial Intelligence methods for the parametrization of physical processes, leveraging explainable AI and physics-informed machine learning and HPC-enabled large scale data understanding and processing, which try to blend machine learning with physical knowledge to achieve solutions that are physically more consistent.
The activity will be carried out in collaboration with Fondazione Bruno Kessler and within the activities of Earth & Climate Spoke of the National Center for High-Performance Computing (HPC). Candidates familiar with physical process simulations are welcome, and basic knowledge of Machine Learning/Deep Learning is recommended.

The need to translate audio input from one language into text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems are now required to be flexible and robust enough to operate in different application scenarios. On one side, the industry calls for key features like real-time processing, domain adaptability, extended language coverage, and the capability to meet application-specific constraints. On the other side, society calls for new efforts towards inclusiveness with respect to specific categories and groups (e.g. gender-sensitivity, customization to the needs of impaired users). Both industry and society face the orthogonal challenges posed by the variability of audio conditions (e.g. background noise, strong speakers’ accent, overlapping speakers). The objective of this PhD is to make ST flexible and robust to these and other factors.

The goal of the thesis is to develop AI models for speech processing which are aware of the computational resources and of the application requirements and are capable of dynamically adapting in order to meet such limitations. This entails not only the search for a trade-off between resources and inference performance but also the possibility to dynamically exploit additional computational resources, eventually expanding the model. The project will address both training and inference phases, starting from state of the art supervised techniques as model compression, neural architecture search, distillation and continual learning and pushing them towards continuous and unsupervised solutions.

Machine and deep learning methods are entering also the industrial sector to automatise 3D monitoring and analysis tasks. The research should investigate the use of AI-based methods to boost photogrammetric 3D inspections for industrial quality control operations. Innovative and advanced AI-based solutions should be developed in order to inspect non-collaborative surfaces (reflective, transparent, etc.) and derive precise 3D results useful for quality control.

Automated planning is successfully used in some application areas for the synthesis of plans to control complex systems. Despite significant progress in the literature, scalability is still a major problem that hinders adoption of planning in a wider range of domains.
In this PhD research in the area of integrative AI, the candidate will study methods for combining modern deep learning approaches with symbolic AI for the automatic adaptation of planning tools. In particular, he/she will develop algorithms to automatically specialize planners on specific domains to improve on scalability by exploiting the characteristics of the target domain extracted automatically by means of machine learning.

Machine learning and deep neural networks have been extensively and successfully used to process multimodal data (e.g., audio, video, environmental data) on powerful computers. At the same time, several challenges remain open to move AI onto low-consuming, resource-constrained devices (e.g., end nodes in an IoT). Recently TinyML approaches are emerging to distribute the intelligence to the far edge of the edge-to-cloud continuum. Exciting research scenarios emerge, spanning from tiny deep learning solutions for inference on resource-constrained platforms based on distillation, quantization, or neural architecture search going up to combing software techniques with novel, innovative hardware supporting TinyML. The complexity grows if we consider moving learning to the edge in order to benefit from the opportunities offered by connected, distributed devices. Motivated by these scenarios, the research aims (i) to define novel hardware/software approaches to optimize AI at the very edge on energy-efficient embedded devices, in particular for audio processing and/or computer vision, but not only; (ii) to explore the potential of distributing and fusing intelligence from heterogeneous nodes of an IoT (iii) to demonstrate the advantages of the investigated approaches in real-world application scenarios, such as those of smart cities.

In the context of the green deal transition and climate change we are looking for candidates willing to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in long and dense timeseries of remote sensing images.
The candidate will be requested to deal with multi-/hyper-spectral images acquired by passive satellite sensors and/or Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. Among the others, data from ESA Copernicus (Sentinels), ASI PRISMA and COSMO-SkyMed will be considered. The goal is to design novel methods able to use temporal correlation to model landcover behaviors, changes and trends for a better understanding of phenomena over the past and the future for detecting trends and changes for modeling and understanding their impacts on climate and environment.
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, deep learning, image/signal processing, statistic/remote sensing, passive/active sensors.

In the last decade, Social Media Platforms have become our main communication hub, encompassing both our personal and our public life. As online social networks have established themselves as important sources or information, they have rapidly changed the landscape of the news media ecosystem on a global scale. News is no longer exclusively broadcast by established sources. Within the participatory environment of these platforms, new opinion leader often actively creates and disseminate news without the restrictions posed upon classical media channels, and often reach large audiences. In this PhD project, we want to investigate how the structural and functional characteristics of online communication networks influence the circulation of news with a focus on disinformation and more broadly junk news. We further want to investigate how such unreliable information diffuses differently across heterogeneously formed communities and characterize the behavior surrounding their reception. We will use methodologies coming from network science, data science and complexity science, integrating the insight about the role taken users that can be obtained by the analysis of the communication network and the associated spreading dynamics with insight about the stance of those users that be automatically extracted using NLP methods.

Supervised learning is a popular mechanism to teach machines vision-based tasks and skills. However, human supervision is a bottleneck for building generic machines that can operate across different contexts, environments and applications. Ideally, machines should develop their own effective and possibly creative strategies for using the sensed data and their experience to continually learn without humans at their side. The research activities related to this PhD position will focus on building novel deep learning-based vision algorithms to teach machines to seamlessly understand environments through 2D or 3D perception.

Precise monitoring of the highly dynamic space radiation environment around Earth is crucial for spacecraft safety.
It supports development of solar particle flux models and allows studies of space weather and of the interaction of radiation belts with Earth's lithosphere.
The project activities include the study of a flat detection geometry to reduce the size of the low energy particle detector.
The project also includes the parametric characterization of the sensors, the development as well as  testing with particle beams of a detector prototype and more in general the integration of the payload.

The LHC experiments produce about 90 petabytes of data per year. Inferring the nature of particles produced in high-energy collisions is crucial for both probing the Standard Model with greater precision and searching for phenomena beyond the Standard Model. In this context, event selection is becoming more difficult than ever before and requires expertise at the border between physics and computer science. During the PhD the student will be guided in exploring and designing Deep Learning algorithms to tackle this problem learning how to apply rigorous Data Science methodologies. The activity will be carried out in collaboration with INFN-TIFPA, Fondazione Bruno Kessler and within the ATLAS experiment at the LHC. Candidates familiar with High Energy Physics are welcome, and basic knowledge of Machine Learning/Deep Learning is recommended.

Research activity conducted during the Ph.D. will focus on the development of mathematical and statistical models to investigate the transmission of emerging and re-emerging pathogens in human populations. This may include the analysis of spatio-temporal patterns characterizing an observed epidemic, the estimation of the contribution of different settings (e.g., households, schools, workplaces, hospitals) in the spread of an infectious diseases, the forecast of potential epidemic trajectories, and the exploration of alternative intervention scenarios (e.g., social-distancing measures, vaccination). Envisioned approaches range from the development and simulation of mechanistic transmission models to the use of statistical inference applied to epidemiological data.

In the last decade, Social Media Platforms have become our main communication hub, encompassing both our personal and our public life. As online social networks have established themselves as important sources or information, they have rapidly changed the landscape of the news media ecosystem on a global scale. News is no longer exclusively broadcast by established sources. Within the participatory environment of these platforms, new opinion leader often actively creates and disseminate news without the restrictions posed upon classical media channels, and often reach large audiences. In this PhD project, we want to investigate how the structural and functional characteristics of online communication networks influence the circulation of news with a focus on disinformation and more broadly junk news. We further want to investigate how such unreliable information diffuses differently across heterogeneously formed communities and characterize the behavior surrounding their reception. We will use methodologies coming from network science, data science and complexity science, integrating the insight about the role taken users that can be obtained by the analysis of the communication network and the associated spreading dynamics with insight about the stance of those users that be automatically extracted using NLP methods.

Deep supervised learning methods, combined with clinical data, have been very successful in predicting disease progression, estimating risk factors and other outcomes of clinical interest in different areas of medicine. However, predictions alone, while useful are not sufficient. Methods that can recommend whether the patients should be treated, and in what way, are necessary. This is known as treatment effect estimation and requires understanding causal relationships between variables found in observational data, such as electronic health records.

In this respect, the work will be focused on developing deep learning methods that can learn causal representations from clinical data, and then provide causal reasoning, including prediction of counterfactuals. The work will build on top of existing approaches, such as Structural Causal Models, as well as more recent approaches, including Recurrent Marginal Structural Networks and Counterfactual Recurrent Networks.

The candidate will have a strong background in machine learning, statistics, mathematics or a related field and will work with real-world patient data related to chronic diseases and critical care. During the PhD the candidate will have the opportunity to collaborate with some of the leading clinical experts in the USA (including from MIT, Mayo Clinic, Cleveland Clinic) as well as experts from leading European institutions.

Energy communities are at the forefront of the EU Green Deal strategy. Since 2016 a number of works have been done by FBK-SE covering the planning of several municipalities and regions based on EnergyPLAN+MOEA. However, these case studies are lagging behind in respect to some aspects: (I) full integration of the multiple decision variables that maximize flexibility, (II) Multi-Node solutions that enhance the synergies between different local, regional, national and transnational scales, (III) holistic approaches among energy-environment-economy-society, (IV) interaction with geospatial models dedicated to land-use, urban planning, mobility, etc., (V) embedding of both climate mitigation and adaptation. Moreover, the current approach is missing the integration of a power flow analysis. Therefore, the overarching goal of this PhD is to develop an innovative multi-objective optimization framework for Smart Energy Communities, including energy and power flow modeling.

The aim of this PhD project is to investigate new technology-based approaches to support adaptive educational paths and/or (partially-)automated personalized support for teachers and/or students. These may include end-user programming for teachers and students for personalizing and co-creating learning activities; human-centred approaches to the design of AI-systems for education; initiatives for support young people and educators that do not have specialized knowledge or technical skills of AI to personalize and tailor AI-based systems to their needs.

The ideal candidate has a background in Computer Science, Psychology or Cognitive Science. Knowledge and experience in data science and basics of Artificial Intelligence are required as well as competences with educational theories. Experience with design of interactive digital technologies, conduction of experimental and in-the-wild studies, and international mobility are a plus for the application and should be acquired during the Phd training

The aim of this PhD project is to investigate new technology-based approaches to support adaptive educational paths and/or (partially-)automated personalized support for teachers and/or students. These may include end-user programming for teachers and students for personalizing and co-creating learning activities; human-centred approaches to the design of AI-systems for education; initiatives for support young people and educators that do not have specialized knowledge or technical skills of AI to personalize and tailor AI-based systems to their needs.

The ideal candidate has a background in Computer Science, Psychology or Cognitive Science. Knowledge and experience in data science and basics of Artificial Intelligence are required as well as competences with educational theories. Experience with design of interactive digital technologies, conduction of experimental and in-the-wild studies, and international mobility are a plus for the application and should be acquired during the Phd training

The aim of this PhD project is to investigate new technology-based approaches to support adaptive educational paths and/or (partially-)automated personalized support for teachers and/or students. These may include end-user programming for teachers and students for personalizing and co-creating learning activities; human-centred approaches to the design of AI-systems for education; initiatives for support young people and educators that do not have specialized knowledge or technical skills of AI to personalize and tailor AI-based systems to their needs.

The ideal candidate has a background in Computer Science, Psychology or Cognitive Science. Knowledge and experience in data science and basics of Artificial Intelligence are required as well as competences with educational theories. Experience with design of interactive digital technologies, conduction of experimental and in-the-wild studies, and international mobility are a plus for the application and should be acquired during the Phd training

The Limadou project gathers some Italian institutions participating in the China Seismo Electromagnetic Satellite (CSES) mission. CSES consists of a constellation of satellites, designed to pursue the deepest campaign of observation of the ionosphere. One of the most important scientific goals of the mission is to look for correlations between transient phenomena in the ionosphere and seismic events. Among payloads, a set of particle detectors is devoted to the detection of charged particles trapped in the Van Allen Belts, to monitor the solar activity and to measure galactic cosmic rays of very low energy. The APP group of the Physics Department in Trento looks for candidates to a PhD programme on the analysis of the scientific data from the payloads on board the CSES-01 and those to be launched on board the satellite CSES-02 in 2022. The student will focus on time-series analyses and participate in the development of the event reconstruction software. These studies will be carried out using the most modern machine learning techniques for clustering and anomaly detection, using full information from CSES payloads. The activity will be carried out in collaboration with INFN-TIFPA, Fondazione Bruno Kessler and the Institute of the High Energy Physics of Beijing. Candidates familiar with the experimental techniques for the detection of charged particles in space are welcome, as well as  basic knowledge of Machine Learning/Deep Learning is recommended.

Water phase changes (evaporation, condensation, freezing) are ubiquitous phenomena of great importance for living beings and in engineering applications. The structure (micro and nano) and chemistry of surfaces control the kinetics and dynamics of these transitions. Plants, for example, offer numerous examples of self-cleaning, antifreeze and water-collecting 1 properties developed over millions of years of evolution. Engineered anti-frosting surfaces find applications in aerospace (ice accretion on aircrafts), heat exchangers (refrigerators), wind turbines and power lines. Structured surfaces that increase evaporation and condensation efficiency are a challenge for Loop Heat Pipes (LHP) and Vapour Chambers that cool electronics on space stations (in microgravity conditions) or in the electronic devices we use on a daily basis. Surfaces that can efficiently collect dew and fog provide a source of water in arid environments and can improve the water recovery system of space stations.

This project will extend the studies carried out during the previous PhD scholarship (within cycle 34, in collaboration with FBK) which focused on anti-frosting and water-harvesting surfaces. The research activity will concern the theoretical study, fabrication, characterisation and experimentation of micro- and nanostructured surfaces with applications in aerospace and energy efficiency. In particular, phenomena of spontaneous jumps of condensation droplets on hydrophobic surfaces, distant coalescence on hydrophilic surfaces and freezing of droplets will be studied. Fabrication techniques may range from micro- and nanolithography, focused ion beam, etching, chemical deposition processes, and polymer moulding. The expected outputs are patents and publications on high impact journals in the field.

Low Gain Avalanche Diodes (LGADs) are silicon sensors that feature internal charge gain. These sensors were initially developed to provide the time information of tracks at high luminosity colliders, with performances reaching single hit time resolutions of a few tens of picoseconds for minimum ionizing particles. These timing capabilities can find applications in spaceborne experiments like: particle identification through time of flight, distinction between incoming and outgoing particles, identification of splash back and punch through of showers in the calorimeter systems, identification of electromagnetic and hadronic showers by observation of the splash back and punch through from calorimeters. A first production of LGADs dedicated to space applications was completed at Fondazione Bruno Kessler (FBK) and is currently being characterized. The activities of this position will be focused on completing the characterization of this first batch, and on the qualification tests to determine whether the LGAD sensors are flight-ready. The sensor characterization will be mainly performed in the laboratories of University of Trento and FBK. The lessons learned in the sensor characterization and qualification will be reflected in the design of future sensors dedicated to spaceborne experiments. Within the timeframe of this position, a second batch of LGADs for space will be produced and characterized.

The phd will investigate new techniques for model-based system and software engineering and formal methods to support the design, mission preparation and operations of space systems. The potential research directions include faul detection, isolation, and recovery for satellites; system level diagnosis and diagnosability based on telemetry; digital twins for satellites.

In the last few years, several companies and researchers have designed AI-based approaches for assessing and selecting the best candidates and talents based on allegedly objective performance criteria. Ideally, the usage of machine learning in assessment has the objective of mitigating the biases which often affect the hiring decisions conducted by human recruiters.
However, several studies have shown that also machine learning algorithms can be characterized by biases. Hence, the goal of this PhD project is to develop and evaluate innovative, fair and explainable machine learning models for inferring candidates’ employability, performance, and individual characteristics.
In particular, the candidate is expected to develop innovative natural language processing and/or multimodal (audio-video) algorithms to extract and to infer information from professional resumes and/or video job interviews in order to evaluate skills, possible future performances and individual characteristics of candidates. Moreover, state-of-the-art and innovative approaches to machine learning fairness will be implemented and evaluated by the student. The outcome of the student may consist in research prototypes that will be tested on Gi-Group data as well as on patents and scientific publications in top-tier conferences (AAAI, ACL, IJCAI, AIES, FaccT, etc.) and journals.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company.

Sensing devices interact in complex environments and their miniaturization and portability call for small form factor and low power consumption. Edge AI is therefore of fundamental importance for robustness, privacy, and long battery operation. The application of AI algorithms to image sensors further constrains technological solutions in resources due to the large amount of generated data. The objective of this project is to study HW-friendly solutions exploiting digital parallel and dedicated HW/SW architectures aimed at solving specific sensing use cases with high efficiency in area and power. The student will have to tackle the challenges with a multidisciplinary point of view, from the study of the sensing problem to the modelling of the solution, and a special focus on the integrated circuit design aspect. Technically speaking, the work will include the development of hardware-friendly algorithms based on deep learning, high-level modeling of an imaging system, simulations of the system behavior in real case scenarios, implementation of the system in a mixed hardware/software platform (which might include PC, SoC, FPGAs, integrated circuits) and characterization of developed system in the lab and on the field. The student will interact with experts in the fields of computer vision and CMOS image sensors gaining a unique combination of background knowledge. The expected outcome is a highly optimized architecture for an imaging system that combines software, firmware and hardware solutions to solve specific, market-driven needs in the consumer electronics, automotive, smart city, robotics, digital industry fields. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company.

THz/IR sensing and imaging systems can expand the capabilities of portable devices beyond the human vision. The objective of this project is to study sensing solutions operating from the THz to the infrared region exploiting antenna-coupled field-effect transistors, including the study and optimization of the devices and the electronic design of integrated readout and control. The student will have to tackle the challenges with a multidisciplinary point of view, from the quasioptical and electromagnetic point of view, the device behaviour in terms of response to an incoming signal and in terms of noise, and a special focus on the integrated circuit design aspect.
Technically speaking, the work will include high-level modeling of devices and sensor architectures, with emphasis on sensitivity and propagation of noise through the readout chain, development of novel pixel and readout architectures, circuit design including schematic and layout, and characterization of fabricated devices in the lab.
The student will interact with experts in the fields of image sensors, THz detectors, and analog circuit design, gaining a unique combination of background knowledge.
The expected outcome is the realization of state-of-the-art image sensors and their validation in a real use-case scenario.
The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and the Company.

In industrial realities, management systems have a categoriesed structure that studies the scope of application discriminating between the different production phases, the type of resources and the dedicated operating departments. The implementation of a quality management system integrates the Quality principle into manufacturing activities is an opportunity to guarantee the quality of research results and to improve and gain recognition for the work done in a research laboratory. In the context of a clean room of R&D production, we want analysing some process flows, which have been identified for this purpose, having as its ultimate goal the CR's already active quality system management model optimisation. This thesis work involves the use of various evidence-based and statistical tools for the definition and visualisation of processes, the identification of possible failures or criticalities and the definition of consequent corrective actions. This approach will define a new model for assessing and managing non-conformities, which are already dealt with the current quality system. The novelty introduced is the development of a management system starting from the knowledge of industrial realities certified and the codification of know-how developed in the MNF CR itself. A practical system declination is the proposal of preventive and corrective actions as tools for non-conformities and criticalities handling as highlighted in the monitoring of process activities. The thesis is organised in two phases. The first phase analyses Dry Etching process as the case study chosen for the definition of the model described above. This choice turns out to be sufficiently complex to represent a self-consistent system that we expect to capture the variability of operational parameters like a general model. For this reason, the second phase involves the study of the generalisation of the validity of the elaborated model to a second domain, relating to the field of deposition processes.

Digital technologies play an ever increasing role in all aspects of human society; this induces a wide range of changes, collectively referred to as Digital Transformation, that, far from being only technological, also cover cultural, organizational, social, managerial aspects of our life.

Artificial Intelligence is a key technology for digital transformation, thanks to its capability to extract information and knowledge from data; this requires the capability to open, analyze and exploit all data available on a given phenomenon, data that are often highly heterogeneous, scattered, and coming from different sources (e.g. open, sensor, free, closed, linked data). This thesis will concentrate on developing a data-driven computational framework, based on AI approaches, able to perform data analysis and prediction in the setting just described. The framework will be developed in the scope of the Digital Hub, a digital platform jointly developed by Dedagroup and Fondazione Bruno Kessler to address digital transformation in different application domains, including Public Administration, Digital Finance, Digital Industry. The validation of the framework will be performed addressing problems in these application domains, by exploiting the data sets and services integrated in the Digital Hub

This PhD aims at investigating and training neural architectures to build large multimodal sequence-to-sequence models able to encode input speech and text in a common space, and also able to decode output text or speech from a common representation. Such a multimodal architecture, if then made multilingual, could become the future unified foundation model for building automatic speech recognition (ASR), text-to-speech synthesis (TTS), speech-to-text translation (S2T) and speech-to-speech translation (S2S) systems from a single backbone.

Downscaling and upscaling of fields of atmospheric variables from modelling and observations by means of Artificial Intelligence techniques. Climate changes and their effects through weather modifications have an enormous impact on countless sectors of society. As a cosequence wether services are facing an increasing demand for comprehensive, robust, timely, reliable and high-resolution information from either moinitoring systems, or weather forecasts or climate projections that provide support to the adaptation and mitigation policies. High-resolution (in space and time) fields are a key tool towards addressing the complex challenges society is facing. Their development requires an intedisciplinary expertise between meteorology, physics, applied mathematics and computer science. The candidate will develop and apply new concepts in the application of Artificial Intelligence (Machine Learning and Deep Learning) for the spatial and temporal downscaling of forecasts, observations and climate projections from coarse-grained sources and vice versa. The activity will be carried out in collaboration with Fondazione Bruno Kessler and within the activities of Earth & Climate Spoke of the National Center for High-Performance Computing (HPC).

Model-based safety assessment (MBSA) is a growing research area in the design of complex safety-critical systems. Starting from requirements and formal models of the system under analysis, automated techniques and tools are used to analyze system correctness and dependability, and to support its certification, automatically constructing safety artifacts such as Fault Trees and FMEA tables.

Objective of the study is to lift MBSA techniques from finite-state systems to the case of hybrid systems that include continuous time and complex dynamics.  The study will investigate three related directions. First, model extension, i.e., the generation of models encompassing faulty behaviors from nominal models, based on a library of predefined faults, specifying the effects and dynamics of faults. Second, the design of engines for the verification and synthesis of safety-related artifacts, based on state-of-the-art parameter synthesis techniques. Finally, the use of contract-based analysis techniques, which exploit the system architecture to perform safety assessment hierarchically.

The Study will be conducted as part of several ongoing research projects carried out at FBK, such as VALU3S (EU funded) and COMPASTA (funded by the European Space Agency).

Cyber deception is a defense strategy, complementary to conventional approaches, used to enhance the security posture of a system. The basic idea of this technique is to deliberately conceal and/or falsify a part of such system by deploying and managing decoys (e.g. "honeypots", "honeynets", etc.), i.e., applications, data, network elements and protocols that appear to malicious actors as a legitimate part of the system, and to which their attacks are misdirected.  The advantage of an effective cyber deception strategy is twofold: on one hand, it depletes attackers' resources while allowing system security tools to take necessary countermeasures; on the other hand, it provides valuable insights on attackers' tactics and techniques, which can be used to improve system's resilience to future attacks and upgrade security policies accordingly.

Although cyber deception has been successfully applied in some scenarios, existing deception approaches lack the flexibility to be seamlessly operated in highly distributed and resource-constrained environments. Indeed, if virtualisation and cloud-native design approaches paved the way for ubiquitous deployment of applications, they widened the attack surface that malicious actors might exploit. In such a scenario, it is practically unfeasible to try to deploy decoys for each and every system's service or application without dramatically depleting resources, especially in edge scenarios, where these are scarcely available.

This calls for a novel approach to cyber deception combining security, networking, cloud and AI technologies, that takes the tradeoff between security and efficiency into account and makes deception strategies more effective in cloud-to-edge environments. The PhD project will tackle the above mentioned challenges from different perspectives, including the dynamic and automated orchestration of decoys, the design and implementation of lightweight and flexible honeypots, the proposition and evaluation of relevant performance indicators and the integration and interaction with DevOps and SecOps tools.

Machine Learning (ML) is nowadays a consolidated technology embedded in various domains of computer science and information technology. In the recent past years, ML has revolutionised cybersecurity applications, with excellent results in various application areas such as: encrypted traffic classification, intrusion detection and prevention, anomaly detection in industrial control systems, identification of malicious software (or malware), among others.

One important research subfield of ML is called Explainable Machine Learning, which relates to understanding the ML model behaviour by means of various techniques such as feature importance scores, influential training data, etc,. Given the complexity of some black-box ML models, it is inherently difficult to understand why they behave the way they do. Understanding how a ML model works and how it takes its decisions is paramount in network security. Indeed, the ability to understand why an event is classified as benign or malicious by an ML-based intrusion detection system allows the ML practitioner to take the necessary counteractions to reduce false positive and false negatives rates, and to make the system more robust to Adversarial Machine Learning attacks.

The objective of this PhD project is to perform fundamental research in the field of ML explainability (understanding how ML algorithms reason their outputs) and to propose novel tools and methodologies for ensuring good performance of ML-based security systems under various working conditions.

The growing demand for low-cost sensors is gaining momentum in various application fields. One of the main drawbacks of low-cost gas sensors, such as chemoresistive ones, is the lack of selectivity, which limits the use of these devices for monitoring specific gases in a complex environment. To overcome this drawback, research currently focuses mainly on the development of novel sensing materials, where the introduction of specific functionalization could lead to increased device selectivity. On the other hand, another interesting approach that can be exploited is the introduction, in the device packaging, of membranes with selective permeation properties, leading to an improvement of the device performance. This approach has the great advantage of not intrinsically modifying the gas sensor, whose development process is well-established, and of being able to tailor the development of the selective membrane according to the gas to be analyzed. In particular, the PhD project proposed here focuses on the development of membranes for the selective permeation of H2, an increasingly energy-important fuel gas for which there is currently a lack of reliable and low-cost technologies for its monitoring.

The affective dimension of word meaning often forms part of our reservoir of common-sense knowledge, and it is reflected in the way we use words. This project aims at producing and evaluating new technologies for recognition of emotional language and possibly other subtle pragmatic aspects of communication. Because there are diverse subtilties in emotional expressions in different languages, the project will devote particular attention in approaching the problem from a multilingual point of view.

Required skills:
Good familiarity and expertise with Computational Linguistics techniques. Experience in machine learning. Good programming skills.

Human-human dialogues are characterized by collaborative behaviours, through which interlocutors achieve their communicative goals. As an example, proactivity (i.e., anticipating user needs during dialogue) and grounding (e.g., posing clarification questions) are two relevant cases that have been investigated from a linguistics perspective. However, such collaborative behaviours are still largely absent in current neural dialogue models. There are several open research challenges in this direction, including investigating how dialogue systems can learn when and how to be collaborative, depending on the dialogue context, and how do we evaluate whether collaborative behaviours have improved the efficacy of dialogue. This PhD project addresses collaborative behaviours in conversational agents from a computational perspective, exploiting the integration of machine learning approaches based on neural models, reinforcement learning, and knowledge-based techniques.

One of the pillars of healthcare digital transformation focuses on the integration of AI-based solutions within the clinician-patient relationships with the aim of monitoring and/or supporting them towards the achievement of healthy functional status.

Examples of these systems are: (I) virtual coaches to support remote monitoring and recommendations for patients affected by nutritional chronic diseases or to support the prevention of the onset of such diseases; (ii) telehealth solutions to enhance care capabilities of health organizations; and, (iii) tools to orchestrate care pathways involving, beside patients, multiple clinical actors.

This Ph.D. works within this context with the aim of designing novel AI-based approaches to trigger the implementation of the next-generation virtual digital assistants.

The area of intervention is very broad since the research areas involved are, for example, knowledge management, human-computer interaction, pervasive computing, machine learning, probabilistic graphical model, natural language processing, and planning.

For this reason the Ph.D. candidate will have the opportunity to explore the virtual digital assistants domain in order to analyse current open challenges, to decide which ones to address and which AI-based approaches she/he will use to tackle such challenges.

The goals of the PhD research are: (i) to study, develop and validate innovative solutions based on AI algorithms to extract geometric and semantic information from digital data (images and 3D models) of cultural heritage; (ii) to propose and test alternative methods that allow to apply machine / deep learning algorithms in contexts with little data availability and with noisy classes, by exploiting integrative AI methods; (iii) to analyze, realize and demonstrate new methods to improve the transparency, interpretability and explainability of AI methods applied to Cultural Heritage 3D data.

The research should tackle the problems with a holistic and integrative approach, considering multi-GPU approaches, increasing learning capabilities and allowing to handle data with noise. Predictive solutions will serve to better analyze, preserve and enhance the Cultural Heritage, as well as to develop VR / AR solutions to support the tourism sector.

Learning-based algorithms for 3D object description, recognition and retrieval suffer from lack of annotated data, unbalanced classes, computationally inefficient processing pipelines and poor generalization ability across different application domains. All these factors together often hamper the employment of 3D processing pipelines in large-scale real-world applications in urban and environmental contexts.

The goal of this Ph.D. position is to conduct research on novel and efficient algorithms for 3D data semantic segmentation and classification using integrative AI approaches that can effectively replace traditional hand-crafted modules to ultimately improve performance, ease deployment and foster scalability. The research should integrate traditional 3D classification methods (RF, 3DCNN, MLP, etc.) with symbolic approaches (KBANN, LTN, etc.) in order to enhance learning capabilities, handle noisy and multi-modal data and deliver a hybrid method able to constraint predictions with a-priori knowledge expressed in terms of logical formulas. A research task should also be dedicated to investigate advanced solutions to handle unbalanced classes in 3D classification problems, considering e.g. oversampling and under-sampling techniques, uneven weight distribution, complex loss functions, etc.

Many application domains require the ability to automatically generate a suitable course of actions that will achieve the desired objectives. Notable examples include the control of truck fleets for logistic problems, the organization of activities of automated production sites, or the synthesis of the missions carried out by unmanned, autonomous robots. Planning and scheduling (P&S) are fundamental research topics in Artificial Intelligence, and increasing interest is being devoted to the problem of dealing with timing and resources. In fact, plans and schedules need to satisfy complex constraints in terms of timing and resource consumption, and must be  optimal or quasi-optimal with respect to given cost functions. The Ph.D. activity will concentrate on the definition of an expressive, formal framework for planning with durative actions and continuous resource consumption, and on devising efficient algorithms for resource-optimal planning. The activity will explore the application of formal methods such as model checking for infinite-state transition systems, and Satisfiability and Optimization Modulo Theories, and will focus on practical problems emerging from the flexible manufacturing domain.

The advent of Industry 4.0 has made it possible to collect huge quantities of data on the operation of complex systems and components, such as production plants, power stations, engines and bearings.  Based on such information, deep learning techniques can be applied to assess the state of the equipment under observation, to detect if anomalous conditions have arised, and to predict the remaining useful lifetime, so that suitable maintenance actions can be planned. Unfortunately, data driven approaches often require very expensive training sessions, and may have problems in learning very rare conditions such as faults. Interestingly, the systems under inspection often come with substantial background knowledge on the structure of the design, the operation conditions, and the typical malfunctions. The goal of this PhD thesis is to empower machine learning algorithms to exploit such background knowledge, thus achieving higher levels of accuracy with less training data.

Runtime verification is a light weight verification technique based on the analysis of system logs. A key factor is that the internal state of the system is not observable, but partial knowledge on its behaviour may be available. The thesis will investigate the use of temporal epistemic logics (i.e. logics of knowledge and believe over time) to specify and verify hyperproperties for runtime verification. Different logical aspects, like distributed knowledge and common knowledge, and the communication between reasoning agents, will be used to model hierarchical architectures for fault detection and identification, and for prognosis. Techniques for planning in belief space will be used for the design of fault reconfiguration policies.

Many artifacts in the development process (requirements, specifications, code) tend to become legacy, hard to understand and to modify. This results in lack of reuse and additional development costs. A reverse engineering activity is necessary to understand what the system is doing. Goal of the thesis is to provide automated techniques to analyse the inherent behavior of legacy artifacts, extract interface specifications, and to support re-engineering activities. The thesis will combine techniques from language learning, applicable to black-box artifacts, and formal techniques for the automated construction of abstractions in the form of extended finite state machines.

Simulation tools are fundamental to predict the evolution of pandemic, and to assess the quality of counter-measures, e.g. the effect of travel restrictions on the spread of the coronavirus. However, they come with two fundamental requirements. The first is the need for a fast reconfiguration of the simulation, in order to be able to describe the mutating scenarios of the pandemics. The second is the ability to produce correct and explainable results, so that they can be trusted and independently validated. The topic of this research is to devise a model-based approach that is able to represent at a high-level the features of a generic pandemic, from which an efficient simulator can be produced. Using formal methods, the results of the simulation are guaranteed to be correct by construction, with proofs that can be properly visualized and independently checked. The activity will be carried out as a collaboration of the Center for Health Emergencies (https://www.fbk.eu/it/health-emergencies/), that played a major role during the ongoing pandemics, and the Center for Digital Industry (https://dicenter.fbk.eu/), a leading centers in  model-based design.

Applications that rely on the most modern sensing devices and technologies and combine complex artificial intelligence tasks are now mainstream. It is sufficient to say, “OK-Google/Alexa/Siri switch on the heating system when the temperature is below 18° C” to appreciate the power of the IoT in combination with an Artificial Intelligence engine. However, the typical approach to enable intelligent applications is cloud-centric, meaning that the intelligence (a home assistant) is hosted in the cloud infrastructure, and the sensor data collected by some IoT devices (a microphone array and a temperature sensor) flow from the cyber-physical-system until reaching a remote endpoint to be processed. Finally, the correct command is transmitted to the IoT actuator (a radiator thermostat). Alternative approaches to this are possible, for instance, by considering a more dynamic and configurable intermediate layer placed between the IoT and the Cloud sides, usually dubbed as the Edge layer.

Generally, a configurable edge layer reduces the required bandwidth and latency and improves users’ privacy. Moreover, if portions of the application intelligence could be hosted in this layer, the IoT device lifetime would be enlarged. However, reconfiguring and deploying an end-to-end processing flow that involves the three aforementioned architectural layers poses major challenges. Select a more efficient detection algorithm from a rich machine learning algorithms library and pushing the “deploy” button of an application dashboard to see the selected algorithm up and running more effectively (according to a given metric) on my smart home devices is still a dream, in most of the cases. Moreover, depending on the hardware capabilities, the application requirements in terms of bandwidth and latency, and the accuracy required for the machine learning task to execute, different end-to-end configurations are possible, all sub-optimal and possibly non-dominated in the Pareto meaning.

The subject of this Ph.D. is to investigate and propose novel optimization and assessment methodologies to efficiently sample such a complex design space in target application sectors such as home, industry, manufacturing, farming, etc. The reference technological environment covers (but is not limited to) embedded device software engineering (micropython, mbed OS, C languages and dialects, etc.), machine learning frameworks deployable on tiny devices (tinyML, TensorFlow lite, etc.), edge-based frameworks (eclipse Kura, edgeX Fundry, etc.) and cloud-based IoT platforms and services with AI support and components (MS Azure, AWS Greengrass, ThingsBoard, etc.).

The advent of Industry 4.0 has made it possible to collect huge quantities of data on the operation of complex systems and components, such as production plants, power stations, engines and bearings.  Based on such information, deep learning techniques can be applied to assess the state of the equipment under observation, to detect if anomalous conditions have arised, and to predict the remaining useful lifetime, so that suitable maintenance actions can be planned. Unfortunately, data driven approaches often require very expensive training sessions, and may have problems in learning very rare conditions such as faults. Interestingly, the systems under inspection often come with substantial background knowledge on the structure of the design, the operation conditions, and the typical malfunctions. The goal of this PhD thesis is to empower machine learning algorithms to exploit such background knowledge, thus achieving higher levels of accuracy with less training data.

Simulation tools are fundamental to predict the evolution of pandemic, and to assess the quality of counter-measures, e.g. the effect of travel restrictions on the spread of the coronavirus. However, they come with two fundamental requirements. The first is the need for a fast reconfiguration of the simulation, in order to be able to describe the mutating scenarios of the pandemics. The second is the ability to produce correct and explainable results, so that they can be trusted and independently validated. The topic of this research is to devise a model-based approach that is able to represent at a high-level the features of a generic pandemic, from which an efficient simulator can be produced. Using formal methods, the results of the simulation are guaranteed to be correct by construction, with proofs that can be properly visualized and independently checked. The activity will be carried out as a collaboration of the Center for Health Emergencies (https://www.fbk.eu/it/health-emergencies/), that played a major role during the ongoing pandemics, and the Center for Digital Industry (https://dicenter.fbk.eu/), a leading centers in  model-based design.

Learning-based algorithms for 3D object description, recognition and retrieval suffer from lack of annotated data, unbalanced classes, computationally inefficient processing pipelines and poor generalization ability across different application domains. All these factors together often hamper the employment of 3D processing pipelines in large-scale real-world applications in urban and environmental contexts.

The goal of this Ph.D. position is to conduct research on novel and efficient algorithms for 3D data semantic segmentation and classification using integrative AI approaches that can effectively replace traditional hand-crafted modules to ultimately improve performance, ease deployment and foster scalability. The research should integrate traditional 3D classification methods (RF, 3DCNN, MLP, etc.) with symbolic approaches (KBANN, LTN, etc.) in order to enhance learning capabilities, handle noisy and multi-modal data and deliver a hybrid method able to constraint predictions with a-priori knowledge expressed in terms of logical formulas. A research task should also be dedicated to investigate advanced solutions to handle unbalanced classes in 3D classification problems, considering e.g. oversampling and under-sampling techniques, uneven weight distribution, complex loss functions, etc.

Many artifacts in the development process (requirements, specifications, code) tend to become legacy, hard to understand and to modify. This results in lack of reuse and additional development costs. A reverse engineering activity is necessary to understand what the system is doing. Goal of the thesis is to provide automated techniques to analyse the inherent behavior of legacy artifacts, extract interface specifications, and to support re-engineering activities. The thesis will combine techniques from language learning, applicable to black-box artifacts, and formal techniques for the automated construction of abstractions in the form of extended finite state machines.

Many application domains require the ability to automatically generate a suitable course of actions that will achieve the desired objectives. Notable examples include the control of truck fleets for logistic problems, the organization of activities of automated production sites, or the synthesis of the missions carried out by unmanned, autonomous robots. Planning and scheduling (P&S) are fundamental research topics in Artificial Intelligence, and increasing interest is being devoted to the problem of dealing with timing and resources. In fact, plans and schedules need to satisfy complex constraints in terms of timing and resource consumption, and must be  optimal or quasi-optimal with respect to given cost functions. The Ph.D. activity will concentrate on the definition of an expressive, formal framework for planning with durative actions and continuous resource consumption, and on devising efficient algorithms for resource-optimal planning. The activity will explore the application of formal methods such as model checking for infinite-state transition systems, and Satisfiability and Optimization Modulo Theories, and will focus on practical problems emerging from the flexible manufacturing domain.

Runtime verification is a light weight verification technique based on the analysis of system logs. A key factor is that the internal state of the system is not observable, but partial knowledge on its behaviour may be available. The thesis will investigate the use of temporal epistemic logics (i.e. logics of knowledge and believe over time) to specify and verify hyperproperties for runtime verification. Different logical aspects, like distributed knowledge and common knowledge, and the communication between reasoning agents, will be used to model hierarchical architectures for fault detection and identification, and for prognosis. Techniques for planning in belief space will be used for the design of fault reconfiguration policies.

Over the last years, industries in all application domains witnessed a fast growth in the adoption of AI techniques based on Machine Learning. The availability of large amounts of data allowed the deployment of solutions like predictive maintenance and various kinds of forecasts.

Traditionally, symbolic and model based techniques are important for various tasks within industries: for example, planning and scheduling are used to automatically synthesize work plans and to control robotic machines; diagnosis is used to identify the source of problems; and verification can certify and debug systems and processes at design time.

In this PhD research, the student will be exposed to both the symbolic and learning perspectives with concrete industrial case studies, researching integrative AI solutions that empower symbolic algorithms and techniques with machine learning as well as learning-based solutions with model-based predictions and analyses.

Cloud adoption is on the rise and promises to offer many advantages by leveraging economies of scale; at the same time, new security and privacy challenges arise.   As an example, consider the protection of data; while in transit and at rest, cryptographic  techniques to guarantee confidentiality and integrity are well-understood and readily available for several different use case scenarios in the cloud. The situation is much less clear for data in use, i.e. during computation, although it is fundamental for trusting cloud service providers without taking for granted their unsupported claims about security assurances especially when sensitive (e.g., healthcare or financial) information is being processed.  To achieve this advanced level of data protection, it is fundamental to design and prove the security of technical enforcement mechanisms of confidentiality and integrity policies in Trusted Execution Environments.  For the usability of these mechanisms, fundamental security services (including key management and attestation) must be developed, their security and risk level formally assessed, and their deployment automated.

The research work to be conducted during the thesis aims to make significant contributions to developing methodologies, automated techniques and tools to assist the development of fundamental services for confidential computing solutions in the cloud with a focus on key management, attestation, and their integration with identity management solutions for both users and machines to establish a root of trust with high assurance.  Applications of interest for the integration of foundational services range from confidential AI, databases, and analytics to confidential ledgers and multiparty collaboration of dataset owners.

References
- Giada Sciarretta, Roberto Carbone, Silvio Ranise, Luca Viganò:
Formal Analysis of Mobile Multi-Factor Authentication with Single Sign-On Login. ACM Trans. Priv. Secur. 23(3): 13:1-13:37 (2020)

- Stefano Berlato, Roberto Carbone, Adam J. Lee, Silvio Ranise:
Exploring Architectures for Cryptographic Access Control Enforcement in the Cloud for Fun and Optimization. AsiaCCS 2020: 208-221

- Edlira Dushku, Md Masoom Rabbani, Mauro Conti, Luigi V. Mancini, Silvio Ranise:
SARA: Secure Asynchronous Remote Attestation for IoT Systems. IEEE Trans. Inf. Forensics Secur. 15: 3123-3136 (2020)

The scholarship aims at exploring synergies between AI techniques (both logic-based and machine learning based) and Process Mining tasks such as discovery, compliance checking, prediction, and recommendation. Special emphasis is placed on the identification of challenges posed by low quality, unbalanced, privacy protected, or scarce/missing data which may affect machine learning based solutions.

Real-time measurements and data collections are still an outstanding challenge, which this thesis program will assist in solving both the surveying and data retrieval and referencing aspects. The goal of the PhD research is develop a photogrammetric methodology to survey and model scenes of any shape, resulting in a scaled semantic 3D point cloud of the surveyed environment. The PhD's goal is to resolve two still open research problems: i) to speed up the digitalization process and ii) to aid data retrieval by using tools that automatically analyze data. In the first step of the research, the candidate will work on automatic real-time image orientation based on V-SLAM techniques. The second step will focus on the integration of machine/deep learning techniques to build a real-time image classification process to help with the automatic referencing of data and info on the 3D digital copy.

Cyber-Physical Systems (CPS) are ubiquitous systems that integrate computation, networking and physical processes. The correctness and dependability of their control software is critical in many high-assurance domains such as space, mobility, and energy. However, their design requires complex component-based hybrid models describing the continuous dynamics of the physical components and the discrete interaction with the control and monitoring components. The objective of this PhD research is that of advancing the state of the art in the formal verification of CPS control design models, integrating various techniques such as SMT-based model checking, contract-based design, abstract interpretation, simulation, test-case generation, and fault injection. The candidate will work on theoretical aspects of the problem as well as its practical applications in relevant case studies, drawn from the domains of aerospace, automotive, railways, and energy.

3D scene understanding using learning-based approaches is becoming largely employed in several application sectors including industry, automotive, surveillance and cultural heritage. Computational algorithms that are traditionally used to process 2D images, such as object detection, tracking and segmentation, are nowadays being successfully extended to process 3D data (e.g. point clouds). However, traditional 3D approaches rarely use the image content directly for their task, but often rely on mid-level representations (e.g. voxels, sparse point clouds) that disregard the rich context provided by images. Moreover, the availability of 3D data is limited because the effort for annotationing it is greater than its 2D counterpart. The goal of this PhD position is to advance the state of the art about 3D scene understanding by focusing on the aspects of learning-based approaches that can potentially leverage different sensor modalities and the lack of human-annotated data.

Cyber-physical systems are ubiquitous systems that integrate computation, networking and physical processes. The correctness and dependability of their control software is critical in many high-assurance domains such as space, mobility, and energy. However, their design requires hybrid models that combine continuous dynamics with discrete states and is typically verified with simulation-based testing. The objective of this PhD research is that of advancing the state of the art in the formal verification of hybrid system models, integrating various techniques such as SMT-based model checking, simulation, test-case generation, and fault injection. The candidate will work on both theoretical aspects of the problem, as well as its practical applications in relevant case studies, drawn from the domains of aerospace, automotive, railways, and energy.

Automated Planning is the problem of synthesizing courses of actions guaranteed to achieve the desired objective, given a formal model of the system being controlled. A class of problems particularly interesting for applications is temporal planning (also called planning and scheduling) where the discrete decisions of "what to do" are coupled with the problem of scheduling (deciding "when to do"). Unfortunately, planning and scheduling techniques suffer from scalability issues and are often unable to cope with the complexity of real-word scenarios, despite the plethora of approaches available in the literature. Recently, efforts such as Deepmind AlphaGO and OpenAI Five hit the headlines, with groundbreaking advancements in the field of reinforcement learning. These techniques are able to automatically learn policies to decide what to do in order to achieve the desired goal. However, they offer no formal guarantee and are not model-based. The research objective of this PhD scholarship is to investigate techniques that combine the formal guarantees offered by automated planning and scheduling with the performance and self-improving capabilities offered by recent advances in deep reinforcement learning to construct self-adaptive planners that can improve over time their performance on specific application scenarios.

The increasing complexity of software systems calls for the development of new methods and tools to design and test software systems characterized by high variability from the point of view of the space of the possible functional configurations, the space of the release architectures, and of the aspects related to dynamic reconfiguration. The goal of this PhD thesis is that of exploring new approaches to the testing, verification and validation of these systems that involve the joint use of model-based and AI based techniques such as planning, machine learning and optimization.

Multi-Access Edge Computing leverages the network's edge to store and process data and applications locally, and provide fast reactions and efficient use of network and computing resources. Future communication systems and networks, both terrestrial and non-terrestrial, will increasingly require solutions based on AI/ML, virtualization and softwarisation techniques besides traditional communication technologies. The goal of this PhD Thesis is to design and explore novel approaches that leverage on the close cooperation with these domains from an overall system perspective.

In the context of the green deal transition we are looking for candidates willing to develop novel methodologies based on machine learning, deep learning pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in long and dense timeseries of remote sensing images.
The candidate will be requested to deal with both multi-/hyper-spectral images acquired by passive satellite sensors and Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. Copernicus data acquired by the new ESA Sentinels will be considered. The goal is to design novel methods able to use temporal correlation to model landcover behaviors, changes and trends for a better understanding of phenomena over the past and the future for a better modeling and understanding of climate change.
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, deep learning, image/signal processing, statistic/remote sensing, passive/active sensors.

Machine learning can impact people when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, several works have shown that training data can be unfairly biased against certain populations and groups, for example those of a particular race, gender, or sexual orientation. Since training data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. This Phd student will work on designing and implementing innovative approaches for fair and explainable machine learning and deep learning algorithms. The selected student will have the possibility of collaborating with the activities of the Human-centric Machine Learning program of the ELLIS society (https://ellis.eu/).

End-2-End models for speech recognition have been steadily improved recently, achieving performance comparable to state-of-the-art systems. This paves the way to the adoption of such solutions also for the extraction of semantically higher information, directly from the raw speech. This allows avoiding approaches based on the combination of speech recognition followed by text processing, with consequent propagation of errors from the intermediate stages. The goal of this thesis is to develop innovative end-to-end systems, eventually based on the transformer model and sequence-to-sequence learning, to address tasks like spoken language understanding, named entity recognition, intent classification and so forth. Ideally, at the end of the doctoral thesis, the candidate would have developed a neural audio processing front end that can be applied to a variety of semantic down-stream tasks.

In the context of dialogues with chatbots it has been shown that endowing neural models with a persona profile is important to produce more coherent and meaningful conversations. Still, the representation of such personas is still very limited, usually based on simple facts. The goal of this PhD Thesis is to make a step forward, trying to grasp more profound characteristics of human personality (such as opinions, values, and beliefs) to drive language generation of conversational agents in multiple domains and languages.

Conversational agents are designed to interact with users in multiple domains on several topics using natural language. Recently end-to-end systems have started to be tested to fight fake news and hate speech in single turn settings. Still, scaling to full dialogue interactions is a challenging topic, requiring world and domain knowledge together with a deep understanding of argumentative tactics. The goal of this PhD Thesis is to overcome the shortcomings of traditional end-to-end applications in which all components are trained from the dialogs themselves, by incorporating several dialogue, argumentation and domain features.

Applications relying on the most modern sensing devices and technologies, also combining complex artificial intelligence tasks are now mainstream. The typical approach to enable intelligent applications is cloud-centric, meaning that the intelligence is hosted in the cloud infrastructure, the sensor data collected by some IoT devices. Shifting intelligence from the cloud to the edge of the network can offer different advantages such as reducing the required bandwidth and latency and also improving users’ privacy. However, reconfigure and deploy an end-to-end processing flow that involves the three aforementioned architectural layers (the cloud, the edge and embedded devices) poses major challenges: many different constraints and trade-offs must be addressed (latency, response time , bandwidth, energy consumption, computational power, computational precision, etc.) The subject of this Ph.D. is to investigate and propose novel optimization (such as e.g. pareto-based optimization) and assessment methodologies to efficiently sample such a complex design space in target application sectors such as home, industry, manufacturing, farming, etc. The reference technological environment covers (but are not limited to) embedded device software engineering (micropython, mbed OS, C languages and dialects, etc.), machine learning frameworks deployable on tiny devices (tinyML, tensorFlow lite, etc.), edge-based frameworks (eclipse Kura, edgeX Fundry, etc.) and cloud-based IoT platforms and services with AI support and components (MS Azure, AWS Greengrass, ThingsBoard, etc.).

Supervised learning is a very popular mechanism to teach machines vision-based tasks and skills. Supervision, however, is a bottleneck for building generic machines that can operate across different contexts, environments and applications, while learning and improving their understanding seamlessly. Ideally, machines should develop their own creative strategies for using the sensed data and their experience to continually learn without humans at their side.
The research activities related to this PhD position will focus on building machine vision algorithms to teach machines to seamlessly understand environments: by exploiting as little supervision as possible, by being independent of the sensor modality being used, and by updating their knowledge when ground truth information becomes incrementally available over time.

The Internet of Things (IoT), including smart objects, wearables, and wireless sensor networks, is becoming a key technology to enable applications and services in several domains. Ultra-low-power embedded devices are pervasive; novel embedded machine learning frameworks have been introduced. Thus, distributing intelligence at the edge is possible, opening exciting research scenarios spanning from novel, innovative hardware for always-on or event-based sensing up to deep learning solutions, federated learning, and continual learning fitting resource-constrained platforms.
Motivated by the challenges of these research scenarios, the research aims to (i) define novel hardware/software approaches to optimize AI at the very edge on energy-efficient embedded devices, in particular for audio processing and/or computer vision; (ii) to explore the potential of distributing and fuse the intelligence in heterogeneous nodes of an IoT (iii) to demonstrate the advantages of the investigated approaches in real-world application scenarios, such as those of smart cities.

Several important problems in modern society, such as pollution and global warming, arise from the inability to achieve cooperation between individuals over a large scale. Recent research is providing a growing evidence of the power of social influence (i.e. peer pressure), in promoting cooperative behavior. This PhD has the goal of developing computational models for modeling human behavior and social interactions and of designing data-driven strategies and incentive schemes for promoting collaboration and cooperation. These approaches will be also compared with gamification strategies in the context of real-world experiments. The work of the student will be in collaboration between two research units of Fondazione Bruno Kessler, MobS (directed by Bruno Lepri) and MoDiS (directed by Annapaola Marconi).

Machine learning and deep neural networks are extensively and successfully used to process audio on powerful computers, while several problems still need to be solved for porting the technology on low consumption devices with limited resources (both in terms of computation power and memory size).
Research is necessary to reduce the redundancy in neural models to make them portable into the internet of things framework. Along this line of research, the Ph.D. thesis will address the problem of end-to-end neural processing for audio classification, keywords spotting, and privacy-preserving audio processing on resource-constrained embedded devices, considering the trade-off between performance and energy efficiency. Advanced explorative research directions will consider how adapting continual learning techniques to low-power end-devices and if approaches such as collaborative machine-learning without centralized training data (i.e. federated learning) can help in privacy-preserving resource-constrained scenarios.

The need to translate audio input from one language into text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems are now required to be flexible and robust enough to operate in different application scenarios and diverse working conditions. On one side, the industry calls for key features like real-time processing, domain adaptability, extended language coverage and the capability to adhere to application-specific constraints (e.g. length or lip-synch constraints in the subtitling and dubbing scenarios). On the other side, the society calls for new efforts towards inclusiveness with respect to specific categories and groups (e.g. gender-sensitivity, customization to the needs of impaired users). Both dimensions (industry and society) face the orthogonal challenges posed by the variability of audio conditions (e.g. background noise, strong speakers’ accent, overlapping speakers). The objective of this PhD is to advance the state of the art in speech translation to make ST flexible and robust to these and other factors.

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in radar and radar sounder images.
The PhD activity will be developed in the context of European Space Agency (ESA) space mission JUpiter ICy moons Explorer (JUICE) in the Jovian system. The candidate will be requested to deal with images acquired from active radar systems including Synthetic Aperture Radar (SAR) images and sub-surface radar sounding data from airborne Earth Observation missions and satellite planetary exploration missions. The activity amins in improving the understanding of subsurface structure and their impact on planetary body climate.
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, deep learning, image/signal processing, statistic/remote sensing/radar.

Gamification principles have proven to be very effective in motivating target users in keeping their engagement within everyday challenges, including dedication to education, use of public transportation, adoption of healthy habits, and so forth. School closures due to the COVID-19 pandemic and thus the sudden change in the management of the students' educational pathways has opened up the need for methods and digital systems able to support teachers in defining educational content and objectives for their classrooms and to keep students engaged in their training path. The goal of this PhD Thesis is to investigate approaches, techniques and tools to design and release educational digital systems for personalized and cooperative learning plans. This will be done exploiting AI techniques for adaptive gamification and will support teachers in the process of defining and monitoring dedicated learning plans for their students. At the same time, it will facilitate learning, will encourage motivation and engagement, will improve student’s participation and cooperation, and will stimulate students to expand their knowledge through dedicated learning plans and personalized feedback.

The Limadou project gathers some Italian institutions participating in the China Seismo Electromagnetic Satellite (CSES) mission. CSES consists of a constellation of satellites, designed to pursue the deepest campaign of observation of the ionosphere. One of the most important scientific goals of the mission is to look for correlations between transient phenomena in the ionosphere and seismic events. Among payloads, a set of particle detectors is devoted to the detection of charged particles trapped in the Van Allen Belts, to monitor the solar activity and to measure galactic cosmic rays of very low energy. The APP group of the Physics Department in Trento looks for candidates to a PhD programme on the analysis of the scientific data from the payloads on board the CSES-01 and those to be launched on board the satellite CSES-02 in 2022. The student will focus on time-series analyses and participate in the development of the event reconstruction software. These studies will be carried out using the most modern machine learning techniques for clustering and anomaly detection, using full information from CSES payloads. The activity will be carried out in collaboration with INFN-TIFPA, Fondazione Bruno Kessler and the Institute of the High Energy Physics of Beijing. Candidates familiar with the experimental techniques for the detection of charged particles in space are welcome, as well as basic knowledge of Machine Learning/Deep Learning is recommended.

Quantum Chromodynamics (QCD) is the theory of the strong nuclear forces. At ultrarelativistic energies the degrees of freedom are quarks and gluons and their interactions can be calculated with weak coupling methods. For sufficiently high energies, the gluon density becomes large leading to strong non-linear effects whose description is the goal of the Color Glass Condensate (CGC) effective theory. It is important to apply the latter for studying observables in the forthcoming Electron Ion Collider (EIC).

We propose to investigate the optimization of quantum simulations on trapped-ion quantum processors. The Ph.D. candidate will explore the use of quantum optimal control techniques to tailor ‘analog’ gates at the laser pulse level, as well as the optimization of ‘digital’ quantum circuits built on predetermined primitive gates. The study will identify the most effective methodology to translate near-term trapped-ion quantum computing into meaningful quantum simulations of microscopic systems.

Neuroimaging methods, like structural, functional, and diffusion MRI as well as MEG/EEG can be used to investigate the anatomical and functional connectivity of the brain. In this project, the candidate will pursue research on machine learning methods for neuroimaging data to study and characterize brain connectivity, with applications to longitudinal studies and clinical practice. The ideal candidate should have a mixed background in neuroimaging techniques and numerate disciplines, like computer science, engineering, physics, or mathematics. This project is in collaboration with the Division of Neurosurgery, S. Chiara Hospital, Trento (IT).

Deep neural networks (DNNs) have reached a prominent position amongst machine learning systems [4, 1] due to an increasing experimental evidence of their flexibility, expressivity, and effectiveness in addressing functional approximation problems. At the same time, a complete and satisfactory mathematical theory, explaining for instance how to optimally design and train a DNN on some specific task, is largely missing. It is worth noticing that typical DNNs need millions or billions of trainable parameters for achieving such outstanding performances, and must execute a proportional amount of arithmetic operations during the inference step. These characteristics make DNNs very demanding in terms of storage, memory, and energy consumption. This creates an obstacle towards their deployment on low-memory and low-power architectures such as embedded devices and microcontroller units (MCUs). For this reason, the use of quantized neural networks (QNNs), that is, of specific DNNs whose parameters take values in small, finite sets and whose activation functions have a finite range, allows to represent the operands with fewer bits with respect to standard DNNs, thus providing significant benefits with respect to digital hardware constraints [10, 5]. In this sense, QNNs have an enormous potential of applications, for instance in biomedical and environmental research, where in-situ analyses of real-time data could be effectively performed by low-powered & portable devices. At the same time, the lack of a rigorous mathematical theory in support of QNNs is even more striking, also due to the non-differentiability properties of such networks, so that a better comprehension of the mathematics involved seems crucial for their effective application. The objectives of the PhD project will be of both theoretical and applicative nature. The theoretical part of the project will consist in studying some mathematical aspects of the theory of DNNs, including: • techniques for layer-wise regularization & approximation of QNNs; • expressivity properties of suitable classes of QNNs;

The Ph.D. student will evaluate control measures against different infectious diseases, both retrospectively and prospectively, by developing mathematical models calibrated against observed epidemiological data and informed by other data relevant to the infection under study. The modelling approach will be tailored to the addressed problems and may include compartmental models, generative models, individual-based simulations as well as Bayesian approaches and will include scenario analysis to compare alternative intervention strategies.

Research activity conducted during the Ph.D. will focus on the development of epidemiological models informed by real-world data aimed at investigating the main determinants of the disease spread in humans. Envisioned approaches range from the study of mechanistic models mimicking the spatio-temporal transmission dynamics of infectious diseases to the use of bayesian approaches applied to detailed epidemiological records.

Interpretability has become a crucial requirement to support translation of reliable AI models into clinical practice. Throughout the graduate course, the candidate will explore how to make a DL model interpretable and reproducible, and she/he will test such methodologies comparing predictive models trained on integrated imaging (CT/PET/MRI or Digital Pathology), omics and clinic data, both publicly available and original, with the final goal of defining a framework or a pipeline leading from the available data to the explainable and repeatable models.

Redox flow batteries (RFBs) are a promising technology for large scale energy storage. In RFBs power and energy are decoupled: the former depends mainly on the size of the stack while the latter on the size tanks containing the redox active species. This feature make RFBs ideal for economical, large- scale energy storage. However, cost reduction are in order for allowing a widespread diffusion of this technology. The required cost reductions involve two main components of the system: the electrolytes and the stack. Both need to be optimized for enabling a large scale diffusion of RFBs.
Flow batteries are a complex system their design and optimization usually leads to a trade-off between cost and performances (energy and power density, efficiency, cycling life). Cell and stack design is a core task required for the development and upscaling of flow battery systems but redox flow cells models can be very complex due to the multitude of physical phenomena that need to be considered: electric fields, fluid flow, mass and specie transport in different components, electrochemical reactions, heat transfer. All these phenomena need to be considered for building a digital twin of cell and stack and enable to identifying cell-limiting mechanisms, forecasting cell performance and optimizing the design. Despite some commercial software (e.g., COMSOL) can support this activity these tools present a lack of flexibility and serious constraints concerning their use on HPC platform as well as their performances (this also limit their use to small scale cells). Furthermore multiscale models that couple detailed cell models and system level models are not currently available.
For the above mentioned reasons, in this PhD topic we propose to develop a multiphysics-multiscale tool aimed at supporting redox flow cell and stack design and upscaling. This tool will also enable design optimization supported by different algorithms. The selected candidate will be in charge of developing the models extending opensource modelling platforms such as OpenFOAM and integrating optimization tools such as Dakota. The platform will be composed of three different main components tightly connected with each other: 1) Multiphysics cell and stack model (using for example OpenFOAM), 2) System level model based on transient 1D-0D models (using OpenFOAM, OpenModelica or python) 3) optimization tool. The outcome from the multiphysics cell model will be used either as input or for computing the parameters required by the system level model. Different type of optimization will be developed based on the final objective.
The simulation models will be validated with experimental data from known chemistries and representative prototypes, and show how new chemistries can be explored. The candidate will be in charge of developing and implementing the physical models, validating the models based on experimental data, integrating different model for building a multiscale tool and integrating the optimization algorithms in the work flow to enable design optimization. In order to enable a strong cross-contamination of ideas and experience, we propose that the candidate will also support the experimental activities related to the validation of redox flow cells with known and new chemistries.

In the last years, mobile mapping systems, such as hand-held devices, drones and ground vehicles equipped with active or passive sensors, have been widely used for precise 3D data generation based on advanced geo-referencing solutions.

According to specific scenario and application requirements and constraints, different techniques can be adopted to tackle positioning and navigation tasks, e.g. solutions based on global navigation satellite system (GNSS), ultra-wide band (UWB) transceivers and 5G mobile network technologies.

The research should investigate different geo-referencing solutions when using mapping platforms, trying to understand potentials and limitations, feasibility and needs in typical geomatics scenarios, such as mapping of urban and forestry areas, precision farming, hazard monitoring, indoor mapping, heritage documentation.

Some topics that should be considered in the research are:

- the forthcoming Galileo high accuracy service, which should be investigated and exploited at the best of it state of implementation;

- the fusion of data from multi-frequency and multi-constellation GNSS receivers and AI-enabled stereo cameras or stereo optical and depth sensors embedded in smart phones;

- alternative positioning solutions such as those based on UWB and 5G technologies.

The mentioned topics should be investigated in particular for the evaluation of GNSS- based positioning performances and impact on the 3D data in mobile mapping applications, in particular in the photogrammetric field. Positioning technologies such as RTK, PPP and PPP-RTK should be considered along with suitable strategies for the mitigation of GNSS signal degradation and complete temporary loss.

The research is expected to advance the knowledge on the impact of most recent geo-referencing solutions in geomatic mapping based on the photogrammetric principles. High-accuracy positioning is expected to improve the quality of 3D geometric and spatial data and modelling to be used in professional applications. The output of the research will be made accessible through scientific papers.

Computational thinking together with digital competences and AI education are emerging as important topics in the field of education and technology enhanced learning. Although several courses and educational material are being developed, there is still a lack of technology-based personalized and inclusive approaches that might be used to improve teaching and learning practices for such concepts.The aim of this PhD project is to investigate new technology-based approaches to develop computational thinking skills, to improve digital competences and to make AI education accessible. These may include (but not limited to) end-user programming for teachers and students for personalizing and co-creating learning activities. The ideal candidate has a background in Computer Science, Psychology or Cognitive Science. Experience with design of interactive digital technologies, conduction of experimental and in-the-wild studies as well as competences with educational theories are a plus for the application and should be acquired during the Phd training. The PhD position is offered in co-tutoring between the i3 research unit of the Digital Society center of FBK and the Department of Psychology and Cognitive Science.

In recent years there has been a growing interest for psychoeducational interventions delivered by means of mobile applications and personal assistants to support self-care of patients, including those coping with chronic conditions. Although the validity of psychoeducation has been proved repeatedly by previous research, the design of effective behavioral intervention technologies for virtual coaching in the area of healthy coping remains a challenge. The aim of the PhD project is to investigate key features of smart coaching solutions for healthy coping interventions that are engaging to use by patients and produce effective outcomes from a clinical perspective. The ideal candidate will be strongly motivated in developing design skills in the field of behavioral intervention technologies and conversational agents for applications in healthcare. The PhD position is offered in co-tutoring between the Digital Health Lab of FBK and DIPSCO.

Game-based motivational systems are emerging as an effective tool to engage users and induce a positive change in human behavior. Games introduce goals, interaction, feedback, problem solving, competition, narrative, and fun learning environments, motivational affordances that can increase end-user engagement and motivation. Gamification has gained significant attention especially in educational contexts, where supporting and retaining students motivation is a constant challenge. Although research has demonstrated that collaborative learning benefits a variety of learning outcomes, while also supporting people’s social, emotional, and psychological well-being, most studies on gamification in the educational context focus on individual work and competitive learning activities, exploiting affordances that highlight each student achievements and progression. The goal of this PhD thesis is to investigate the potential of combining motivational gamification mechanics and social and interactive elements of collaborative learning, analyzing the impact in terms of
students’ achievements, engagement, participation and cooperation. The ideal candidate has a background in Computer Science or Cognitive Science. Game design, educational and cognitive psychology, motivation theories, knowledge on designing and conducting experimental studies, experience with quantitative and qualitative data analysis techniques are a plus for the application and should be acquired during the Phd training.

The Phd candidate will deal with the analysis of olfactory information using digital methods. In particular, s/he will analyse how odors are described in multiple languages, and how smell-related terminology has evolved over time. S/he will also contribute to the development of a system to extract olfactory information from texts, and to the linguistic analysis and evaluation of the system output.

In recent years there has been a growing interest in conversational AI, and a number of conversational systems are now operative in various sectors, including call centres. This situation has made available a huge amount of user-machine interactions, which have a high potential to be used to improve the system performance. As an example, the capacity to detect the emotional content of the conversation would allow the system to respond in a more appropriate way to the user requests. This PhD grant addresses some of the scientific challenges which are behind the interpretation of very large-scale conversational data, including managing noisy data, topic clustering, semi-supervised intent classification, and emotion detection. The resultant of these techniques will be applied to develop empathic chatbots able to model their answers, in real time, on the basis of the human detected emotions expressed during the conversations.

Required/Preferred Candidate Skills and Competencies: Required: Master’s Degree in Science, Computing & Technology, Statistics, Engineering or Mathematics Preferred: Documented experiences in the use Machine Learning techniques applied to real data.

The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by the Company.

Some recent works have shown that the combination of high-resolution satellite imagery and machine learning/deep learning techniques have proven useful for a range of socio-economic and sustainability-related tasks, from poverty prediction to population mapping, from forest and water quality monitoring to the mapping of informal economic activities and settlements, etc. This PhD project aims at designing novel deep learning approaches and novel ways for combining satellite imagery collected at different temporal and spatial resolutions, combining different types of data (for example, optical + radar), and/or combining satellite imagery with other relevant data such as information captured by mobile phones. Moreover, special focus will be dedicated to address applications characterized by the limited amount of reference training information (e.g., property valuation, spatial wealth distribution, exposure to respiratory diseases, etc.). The ideal candidate is strongly motivated to develop machine learning and remote sensing skills focusing on deep learning, satellite imagery and multi-modal approaches, as well as interested in applications to socio-economic and sustainability challenges. The project will be supervised by Bruno Lepri (FBK), Emanuele Strano and Mattia Marconcini (MindEarth).

Required/Preferred Candidate Skills and Competencies: Background in computer vision with knowledge on object recognition, image segmentation, deep learning techniques. Interest for applications to visual scene understanding (in particular, urban environments).

This PhD project is focused on using deep learning approaches, computer vision and photogrammetry for understanding visual scenes, in particular related to urban environments and people’s behaviours. The project aims at designing novel deep neural network architectures able to exploit multiple sources of data efficiently and to detect people's behaviours, objects, vehicles, in crowded environments such as streets, squares, malls, etc. For example, the project might involve street-view imagery in conjunction with satellite imagery, 3D data, etc. to predict urban outcomes and people’s behaviour. In addition, the candidate will work on generative models (e.g. Generative Adversarial Networks) to augment training data on urban and behavioral patterns (e.g. people movements), which have to be realistic and capture the high diversity of urban forms and lifestyles observed across the globe. Special emphasis will be given to exploit synthetically generated data within GAN architectures. The ideal candidate is strongly motivated to develop machine learning skills focusing on deep learning computer vision and multi-modal approaches. The project will be supervised by Bruno Lepri (FBK), Nicu Sebe (DISI), and Emanuele Strano (MindEarth).

Required/Preferred Candidate Skills and Competencies: Background in computer vision with knowledge on object recognition, image segmentation, deep learning techniques. Interest for applications to visual scene understanding (in particular, urban environments).

Hydrogen is the most promising among the potential green gases, an essential energy carrier to enable a deep decarbonization, for the sectors difficult to abate, such as heavy industry and heavy mobility. Hydrogen indeed must be extracted by water through electrolysis or other materials of biological origin and wastes. One of the most urgent needs to solve is the scaling up of the sector involving several solutions in the way hydrogen is stored, moved, transported in between production and utilization. Beyond compression, one promising direction for some sectors is that of energy carriers, such as liquid hydrogen and liquid organic hydrogen carriers. Among these, ammonia. For its specific characteristics, ammonia could be an ideal carrier in terms of physical and chemical properties, energy density, enabling an efficient logistic and an ideal use in the hydrogen chain. One of the gaps is its synthesis and its direct utilization. This is potentially feasible through innovative technologies, such as reversible Solid Oxide Cells. The PhD will focus on these two dimensions to enable a safe and efficient generation of ammonia in (co)-electrolysis processes through Solid-State Ammonia Synthesis (SSAS) and its utilization in Direct Ammonia Fuel Cells The PhD will focus on both modelling and engineering as well as on experimental and validation activities for cell and short stack based Solid State technology. The activities will include: • Preliminary study on enabling key technologies for the ammonia synthesis; • Engineering study for direct ammonia synthesis, to design and develop both the single components level and the overall integrated system in terms of sizing, Balance of Plant, integration layout and controls; • Demonstration on lab scale of ammonia synthesis through SSAS process and its utilization in Direct Ammonia Fuel Cells based on Solid oxide and/or Proton Conductive Ceramic technologies.

Required/Preferred Candidate Skills and Competencies:
- Competences in energy engineering;
- Knowledge of the Hydrogen chain;
- Knowledge on conversion processes using both Electrolyser and fuel cells;
- Lab training in use of hydrogen related compounds, including hydrogen carriers.

Among the low temperature electrolysis processes, two main approaches are extensively documented: alkaline water electrolysis (AWE) and proton exchange membrane electrolysis cell (PEMWE). AWE is a well-established and durable technology yet with many shortcomings being a large footprint, difficulties in handling the liquid alkaline electrolyte, and insufficient response time. Anion exchange membrane water electrolysis (AEMWE) can potentially combine the beneficial features of the PEMWE and AWE technologies, low cost, raw materials that do not raise concerns in terms of supply bottlenecks, electrodes that do not include platinum group metals (PGM), stainless steel porous transport layers (PTL) and bipolar plates (BPP), a compact design, the adoption of feeds based on noncorrosive liquids (low concentration alkali or pure water), and differential pressure operation. However, as of today AEMWE is limited by AEMs exhibiting an insufficient ionic conductivity as well as a poor chemical and thermal stability. The thesis will focus onto the development and testing of innovative cell layout and materials and in parallel on the improvement/optimization of existing AEM WEL cells. A second aspect of the work is the development of a novel concept stack AEMWE, based different geometry of flow field and electrolyte distribution to extend the dynamic range of operation as well as the reduction of gas cross over to achieve an high purity hydrogen output. The key concept is to reduce the voltage and increase the current density. The approach of the work will articulate around the following aspects: design of high surface area cell, developing suitable support material for this approach which needs to be active towards water dissociation, selection of the more performant components and finally validation of the AEM-WEL stack layut best candidate.

Required/Preferred Candidate Skills and Competencies: consistent, diligent, independent, innovative, good command of English. Knowledge of Italian is strongly preferred.

AEM (Anionic Exchange Membrane) technology represents one of the most advanced and promising technology for the low temperature electrolysis process for the production of green hydrogen. The most important advantage is given by the low cost, if compared with other similar technologies, like the PEM based electrolysis. The cost reduction is given mainly by the adoption of non-precious-metal compounds in the catalytic layers. Currently the AEM technology is not fully mature for large commercial applications, due to the gap of the AEM durability and efficiency, compared to the other, mature technologies. The main target of this research activity will be focused on the development of advanced deposition technologies to improve the AEM performances in terms of efficiency and durability. More in particular, the research activity will be focused on electrospinning coatings of catalytic nanofibers and on spray coatings technologies. Dry synthesis technologies based on the use of plasma and chemical-plasma hybrid processes will be applied too, as well as sophisticated surface analysis techniques. Physical vapor deposition, surface plasma functionalization and atomic layer deposition techniques will be employed for the membrane surface and bulk properties modification following as much as possible on-pot and scalable methods. X-ray photoelectron and Auger electron spectroscopies will be used to investigate the chemical properties of the membrane’s surfaces related to the searched functionalities, along with the physical properties and mechanical stability of the membranes which will be studied by means of electrical, mechanical and stress measurements. AEM will be characterized about V-I behaviour and impedance measurements to investigate limiting transfer mechanism and main barriers. The final target and expected outcome will also include a preliminary analysis of the scale up of the coating technology to a mass production level, for commercial electrolyzer applications.

Required/Preferred Candidate Skills and Competencies:
- Catalytic materials;
- Advanced nanomaterials for energy applications;
- Deposition techniques for nanostructured layers and thin films;
- Electrolysis technologies.

A clinical case is a statement of a clinical practice, presenting the reason for a clinical visit, the description of physical exams, and the assessment of the patient’s situation. Clinical cases (e.g. discharge summaries, clinical cases published in journals, and clinical cases from medical training resources) provide a very valuable source of information for data-driven technologies aiming at predicting clinical outcomes and patient behaviors. This three-year PhD offers a unique context of a collaboration between FBK, specifically the NLP group, and a Swiss multinational healthcare company, worldwide leader in biomedical research. The research will focus on a number of application oriented tasks, including automatic recognition of clinical entities (e.g. pathologies, symptoms, procedures, and body parts, according to standard clinical taxonomies such as ICD-9, ICD-10 and SNOMED-CT); detection of temporal information (i.e. events, time expressions and temporal relations, according to the THYME TimeML standard), and factuality information (e.g. event factuality values, assessment of the effect of negation, uncertainty and hedge expressions). Italian will be the major language of clinical cases, although technologies will be experimented on other languages. The goal of the PhD is both to advance the state of the art for clinical case analysis for the Italian language, and to deliver prototype applications, which can be further made operative in real settings (e.g. hospitals). The candidate will have the unique opportunity to explore different domains (Natural Language Processing, Machine Learning, Health & Well-Being) being directly coached by very experienced teammates. The involved PhD will work in an international environment, collaborating with a healthcare company, with worldwide presence. The candidate will work both at FBK (Trento) and at the abovementioned company’s premises (both in Italy and abroad).

Required/Preferred Candidate Skills and Competencies: the candidate should possess basic knowledge on Natural Language Processing and Machine Learning techniques (particularly deep learning architectures). Experience on biomedical data will be a plus. Basic programming skills (e.g. Python) would complete the profile. Proficiency in English is required, basic knowledge of Italian preferable.

Dialogue agents usually work in limited domains with clear and well defined policies, with little adaptation capabilities
to the contextual and social situations. In this scenario it has been shown that endowing neural models with a consistent user/machine
profile is important to produce more coherent and natural conversations. Still, the representation of such profiles is very limited, usually based on simple facts. The goal of this PhD Thesis is investigating end-to-end approaches and generation strategies such as guided decoding, in combination with large pre-trained language models and external knowledge, in order to improve the naturalness of
dialogues. During the PhD we will always be mindful of the societal impact of the technologies we develop.

In recent years there has been a growing interest for psychoeducational interventions delivered by means of mobile applications and personal assistants to support self-care of patients, including those coping with chronic conditions. Although the validity of psychoeducation has been proved repeatedly by previous research, the design of effective behavioral intervention technologies for virtual coaching in the area of healthy coping remains a challenge. The aim of the PhD project is to investigate key features of smart coaching solutions for healthy coping interventions that are engaging to use by patients and produce effective outcomes from a clinical perspective. The ideal candidate will be strongly motivated in developing design skills in the field of behavioral intervention technologies and conversational agents for applications in healthcare. The PhD position is offered in co-tutoring between the Digital Health Lab of FBK and UNIPD.

With this phd, we want to investigate how recommendation systems can take advantage of integrating background knowledge expressed in some symbolic form as e.g. logical formulas with standard numeric optimization method, based on machine learning approaches.
In particular we want to investigate how the state-of-the art methods for recommended systems based on embeddings can be complemented with logical reasoning on formulas that expresses structural properties and constraints about properties and relations between users and items.

Machine learning and deep neural networks are extensively and successfully used to process audio on powerful computers, while several problems still need to be solved for porting the technology on low consumption devices with limited resources (both in terms of computation power and memory size).
Research is necessary to reduce the redundancy in neural models to make them portable into the internet of things framework. Along this line of research, the Ph.D. thesis will address the problem of end-to-end neural processing for audio classification, keywords spotting, and privacy-preserving audio processing on resource-constrained embedded devices, considering the trade-off between performance and energy efficiency. Advanced explorative research directions will consider how adapting continual learning techniques to low-power end-devices and if approaches such as collaborative machine-learning without centralized training data (i.e. federated learning) can help in privacy-preserving resource-constrained scenarios.

The Internet of Things (IoT), including smart objects, wearables, and wireless sensor networks, is becoming a key technology to enable applications and services in several domains. Ultra-low-power embedded devices are pervasive; novel embedded machine learning frameworks have been introduced. Thus, distributing intelligence at the edge is possible, opening exciting research scenarios spanning from novel, innovative hardware for always-on or event-based sensing up to deep learning solutions, federated learning, and continual learning fitting resource-constrained platforms.
Motivated by the challenges of these research scenarios, the research aims to (i) define novel hardware/software approaches to optimize AI at the very edge on energy-efficient embedded devices, in particular for audio processing and/or computer vision; (ii) to explore the potential of distributing and fuse the intelligence in heterogeneous nodes of an IoT (iii) to demonstrate the advantages of the investigated approaches in real-world application scenarios, such as those of smart cities.

As demonstrated by recent events, telecommunications networks’ importance for economic activity and simple human communication cannot be understated. However, while networks held up remarkably well despite a near-doubling of their expected load, their resiliency is not infinite, and their role as information carriers makes them useful targets for malicious actors, from crooks to state-sponsored agents. The advent of programmable ASICs offers a unique opportunity for researchers to observe and customize network device behaviors at a level of detail and time resolution unthinkable with traditional approaches. These, in turn, enable the development and application of new or previously unsuitable strategies for on-the-fly fault detection, isolation and recovery policies, including aspects such as detailed timing and direction of error propagation and intrusion detection and isolation.
This thesis combines the topics of programmable networks and advanced fault and intrusion detections and recovery/isolation, with the aim of improving the resiliency of large-scale telecommunications networks against both failures and targeted attacks.

Machine Learning (ML) is nowadays a consolidated technology embedded in various domains of computer science and information technology. In the recent past years, ML has revolutionised cybersecurity applications, with excellent results in various application areas such as: encrypted traffic classification, intrusion detection and prevention, anomaly detection in industrial control systems, identification of malicious software (or malware), among others.
One important research subfield of ML is called Explainable Machine Learning, which relates to understanding the ML model behaviour by means of various techniques such as feature importance scores, influential training data, etc,. Given the complexity of some black-box ML models, it is inherently difficult to understand why they behave the way they do.
Understanding how a ML model works and how it takes its decisions is paramount in network security. Indeed, the ability to understand why an event is classified as benign or malicious by an ML-based intrusion detection system allows the ML practitioner to take the necessary counteractions to reduce false positive and false negatives rates, and to make the system more robust to Adversarial Machine Learning attacks.

The objective of this thesis is to perform fundamental research in the field of ML explainability (understanding how ML algorithms reason their outputs) and to propose novel tools and methodologies for ensuring good performance of ML-based security systems under various working conditions.

Space and avionics systems are reaching an unprecedented degree of complexity. The process of safety analysis attempts to characterize the likelihood of faults and failures, and to assess the effectiveness of the adopted mitigation measures. Unfortunately, traditional techniques are becoming ineffective, unable to deal with large-scale systems. This thesis will investigate novel methods for safety analysis, based on the adoption of formal models of system and software (nominal and faulty) behaviors. Particularly interesting are the analysis of timing aspects in the propagation of multiple faults to failures and errors, the ability to explain the causality of propagation, and the definition of techniques for on-the-fly fault detection, isolation and recovery policies.

The PhD project aims at the investigation of innovative functional nanomaterials towards the selective detection of gaseous compounds. The combined use of microfabrication process, silicon functionalization and solid-state gas sensor technology enables a new breakthrough approach in the development of high-performing gas sensing devices, useful for different applications such as indoor and outdoor air quality monitoring, precision farming and medical screening.

Advanced nanostructured materials can be exploited both to improve the gas sensor performance, and to develop innovative gas monitoring tool, such as monolithic micro-gas chromatographs. These innovative gas monitoring systems requires an interdisciplinary investigation into the operating principles of the gas chromatographic technique. The synergistic effect of the system fluid dynamics, combined with the chemistry of surface interactions, represents the basis of this technology. In particular, the chemistry of heterogeneous solid-gas and/or liquid-gas interactions play a key role in the separation and detection of the analyzed molecules. The adequate functionalization with polar or non-polar stationary phases of the pre-concentrator and of the chromatographic microcolumn, developed by means of silicon microfabrication techniques, is crucial to obtain an adequate separation of the analytes. Likewise, the development and deposition of specific nanostructured materials, which act as an active sensing layer in the sensing platform, enables the detection of previously separated compounds

This PhD grant will exploit cross-disciplinary competences in three areas, i.e., deep learning, argumentation mining and conversational AI, to support a broader and innovative view of explainable AI. The goal is to advance the state of the art in explanatory dialogues through the capacity to automatically detect the quality of an argument. The grant is related to the ANTIDOTE project (Argumentation-Driven explainable artificial intelligence for digital medicine), which fosters an integrated vision of explainable AI, where low level characteristics of the deep learning process are combined with higher level schemas proper of the human argumentation capacity. The research will focus on a number of deep learning tasks in the medical domain, where the need for high quality explanations for clinical cases deliberation is more critical than in other domains.

The aim of this thesis is investigating how to develop, exploit, and combine techniques and approaches borrowed from different research fields, ranging from logic to artificial intelligence, from model checking to statistics, to advance the existing services for process mining.
To this purpose, several are the challenges to be faced in the work as, for example, (i) the capability to represent and exploit secondary aspects for business processes such as data, time, resources; (ii) the capability to align execution information with models; (iii) the capability to manage and reason on extremely large quantity of data (big data);

In knowledge-intensive Digital Society applications it is essential to be able to integrate heterogeneous, large and uncertain knowledge sources and infer new knowledge emerging from such an integration process. We are interested in developing new methods for tackling these problems, both from the foundational and the practical point of view. We identify two possible directions: 1) Ontology-mediated transformation of knowledge structures for efficient migration
We aim at developing solutions for migration of data between heterogeneous representation schemas: an ontology is used as an abstract model of the domain of data; ontology aligned schemas will provide data access as a Virtual Knowledge Graph.
We are interested in automatizing ontology mappings from/to resources and logic-based generation migration rules across resources. 2) Reasoning with weighted information to handle context and exceptions. We recently considered a solution (justifiable exceptions) for reasoning over ontological knowledge that allows for exceptional instances. However, in real-world data it is often useful to quantify the degree of exceptionality or probability of an axiom. We are thus interested in studying defeasible reasoning in (contextualized) description logics by integrating numerical reasoning as well as novel methods of explainability in AI.

Integrating formal verification and testing for parametric software product lines

The increasing complexity and configurability of systems requires the development of new methods and tools to design and test parametric software systems and product lines characterized by variability from the point of view of the space of the possible functional configurations, the space of the deployment architectures, and of the aspects related to their dynamic reconfiguration at run-time. This Ph.D. thesis aims at defining novel approaches to the testing, verification and validation of this class of systems exploiting the integration of formal verification techniques and software testing approaches. On the one hand, formal techniques have the potential to analyze and cover all the behaviors of a software system; on the other hand, testing techniques are able to ensure adequate levels of coverage also in case of large and complex systems. The challenge here is the search for new directions and methods to interleave the two approaches in the specific case of large parametric systems, in order to optimize the balance between the adequacy of the coverage of the system's behaviors and the effort of the overall verification activity.

Reconfigurable and trustworthy pandemic simulation

Simulation tools are fundamental to predict the evolution of pandemic, and to assess the quality of counter-measures, e.g. the effect of travel restrictions on the spread of the coronavirus. However, they come with two fundamental requirements. The first is the need for a fast reconfiguration of the simulation, in order to be able to describe the mutating scenarios of the pandemics. The second is the ability to produce correct and explainable results, so that they can be trusted and independently validated. The topic of this research is to devise a model-based approach that is able to represent at a high-level the features of a generic pandemic, from which an efficient simulator can be produced. By means of formal methods, the results of the simulation are guaranteed to be correct by construction, with proofs that can be properly visualized and independently checked. The activity will be carried out as a collaboration of the Center for Health Emergencies (https://www.fbk.eu/it/health-emergencies/), that played a major role during the ongoing pandemics, and the Center for Digital Industry (https://dicenter.fbk.eu/), a leading centers in model-based design.

Condition monitoring and predictive maintenance of complex industrial systems: Model-based reasoning meets Data Science

The advent of Industry 4.0 has made it possible to collect huge quantities of data on the operation of complex systems and components, such as production plants, power stations, engines and bearings. Based on such information, deep learning techniques can be applied to assess the state of the equipment under observation, to detect if anomalous conditions have arised, and to predict the remaining useful lifetime, so that suitable maintenance actions can be planned. Unfortunately, data driven approaches often require very expensive training sessions, and may have problems in learning very rare conditions such as faults. Interestingly, the systems under inspection often come with substantial background knowledge on the structure of the design, the operation conditions, and the typical malfunctions. The goal of this PhD thesis is to empower machine learning algorithms to exploit such background knowledge, thus achieving higher levels of accuracy with less training data.

Planning and scheduling with time and resource constraints for flexible manufacturing

Many application domains require the ability to automatically generate a suitable course of actions that will achieve the desired objectives. Notable examples include the control of truck fleets for logistic problems, the organization of activities of automated production sites, or the synthesis of the missions carried out by unmanned, autonomous robots. Planning and scheduling (P&S) are fundamental research topics in Artificial Intelligence, and increasing interest is being devoted to the problem of dealing with timing and resources. In fact, plans and schedules need to satisfy complex constraints in terms of timing and resource consumption, and must be optimal or quasi-optimal with respect to given cost functions. The Ph.D. activity will concentrate on the definition of an expressive, formal framework for planning with durative actions and continuous resource consumption, and on devising efficient algorithms for resource-optimal planning. The activity will explore the application of formal methods such as model checking for infinite-state transition systems, and Satisfiability and Optimization Modulo Theories, and will focus on practical problems emerging from the flexible manufacturing domain.

Meta-learning for efficient 3D representations

Learning-based algorithms for 3D object description, recognition and retrieval suffer from lack of annotated data, computationally inefficient processing pipelines and poor generalisation ability across different application domains, such as robotic manipulation, automotive, and augmented and virtual reality. All these factors together often hinder the employment of 3D processing pipelines in real-world applications. The goal of this Ph.D. position is to conduct research on novel efficient algorithms for 3D feature representations using deep learning that can effectively replace traditional hand-crafted modules to ultimately improve performance, ease deployment and foster scalability.

Deep neural networks are being extensively and successfully used in a variety of audio processing tasks. However, neural solutions typically are computationally eager, in particular during training, and are not suitable for low consumption devices with limited resources (both in terms of computation power and memory footprint). TinyML (or tinyAI) is a branch of neural machine learning that investigates strategies to make AI portable on constrained platforms as those typically available in internet of things frameworks. Along this line of research, the Ph.D. thesis will address the problem of end-to-end neural processing for audio classification, keywords spotting, and privacy-preserving audio processing on resource-constrained embedded devices, considering the trade-off between performance and energy efficiency. Advanced explorative research directions will consider how adapting continual learning techniques to low-power end-devices and if approaches such as collaborative machine-learning without centralized training data (i.e. federated learning) can help in privacy-preserving resource-constrained scenarios.

Cloud adoption is on the rise and promises to offer many advantages by leveraging economies of scale; at the same time, new security and privacy challenges arise. As an example, consider the protection of data; while in transit and at rest, cryptographic techniques to guarantee confidentiality and integrity are well-understood and readily available for several different use case scenarios in the cloud. The situation is much less clear for data in use, i.e. during computation, although it is fundamental for trusting cloud service providers without taking for granted their unsupported claims about security assurances especially when sensitive (e.g., healthcare or financial) information is being processed. To achieve this advanced level of data protection, it is fundamental to design and prove the security of technical enforcement mechanisms of confidentiality and integrity policies in Trusted Execution Environments. For the usability of these mechanisms, fundamental security services (including key management and attestation) must be developed, their security and risk level formally assessed, and their deployment automated. The research work to be conducted during the thesis aims to make significant contributions to developing methodologies, automated techniques and tools to assist the development of fundamental services for confidential computing solutions in the cloud with a focus on key management, attestation, and their integration with identity management solutions for both users and machines to establish a root of trust with high assurance. Applications of interest for the integration of foundational services range from confidential AI, databases, and analytics to confidential ledgers and multiparty collaboration of dataset owners.

Autonomy requires the ability to devise a suitable course of actions, execute the resulting plan, monitor its execution, detect the presence of faults that may have occurred, identify their nature, recover from them, and replan. In addition to achieving the desired goals, planning is required to optimal with respect to multiple criteria, including resource consumption, timings, and tolerance to faults. The problem addressed in this thesis is to define a trustworthy architecture for autonomy, whose correct operation can be formally proved. This shall be carried out by representing planning, runtime monitoring, fault detection, identification and recovery (FDIR), and robustness evaluation in a unique logic-based framework. Satisfiability Modulo Theory will be used to represent with real-time constraints, nondeterminism, resource consumption and temporally extended goals. Within this framework, the algorithms for planning, monitoring and FDIR will be inspired by modern model checking procedures based on incremental abstraction-refinement to deal with infinite state spaces.

Chemoresistive gas sensors are high-performance sensing devices with very attractive properties, such as high sensitivity, small size and easy integration into portable eletronics and IoT platform. However, their low selectivity is limiting their full potential in many applications. In order to overcome this shortcoming, the use of artificial intellingence approaches for data data analysis is the most promising. Therefore, the adoption of specific sensor arrays equipped with dedicated algorythm turned out to be the successful strategy for the development of optimized sensing platform for medical applications and the indoor air quality monitoring. The PhD project here proposed aims at the investigation of innovative solutions for data analysis of chemoresistive gas sensors, especially for outdoor air quality monitoring and precision agriculture.

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects’ data access is attained by displacing workloads from the central cloud to edge devices, within the so-called cloud-to-edge continuum. By doing so, fog computing enables low-latency and privacy-preserving processing of data close to where it is actually produced and consumed. In addition to that, it overcomes communication bottlenecks and reduces costs, representing a key step towards the pervasive uptake of IoT/edge-based services.
This PhD project has the ambition of investigating dynamic resource orchestration methods and algorithms to smartly deploy applications in fog computing environments, with the aim of catering to the needs of applications while optimising the utilisation of the infrastructure. The work will cover both theoretical and practical aspects.

Fog Computing recently emerged as the specialisation of cloud computing to store, manage, and process information close to the edge, where data is actually produced and consumed, to support different IoT applications and use cases that are sensitive to e.g. latency, privacy, bandwidth, etc. Given the amount and heterogeneity of devices, data and applications involved, resilience to anomalies, misconfigurations and security threats is now becoming a major concern for all the involved stakeholders. In this context, Artificial Intelligence (AI) is offering powerful instruments (e.g. Deep Learning) to effectively detect attack patterns and discover threats. The goal of the proposed PhD position is to study and develop novel methods and algorithms for anomaly detection and mitigation in fog-enabled computing environments, with specific attention to balancing high detection accuracy and minimal overhead. AI and other techniques will support the dynamic detection of anomalies, the runtime execution of security functions, the rerouting of traffic and the reconfiguration of existing policies.

Machine learning and deep neural networks are extensively and successfully used to process audio on powerful computers,  while several problems still need to be solved for porting the technology on low consumption devices with limited resources  (both in terms of computation power and memory size).

Research is necessary to reduce the redundancy in neural models to make them portable into the internet of things framework. Along this line of research, the PhD thesis will address the problem of end-to-end neural processing for audio classification, keywords spotting and small vocabulary speech recognition on resource-constrained embedded devices, considering the trade-off between performance and energy efficiency.

The combination of Cloud and Edge Computing is a key-enabler for the deployment of innovative applications and services in scenarios such as cooperative, connected and automated mobility, industry 4.0, home automation and smart cities. On the positive side, the new paradigm promises to enable the exploitation of the potentially unlimited computational capabilities of the Cloud and the low-latency processing of the Edge. On the negative side, the distributed nature of Edge computing entails new security and privacy challenges. Indeed, data generated by these applications may be processed by many devices before eventually being stored in the Cloud. With the new General Data Protection Regulation (GDPR [1]), preserving data privacy is no more an option; it has become a pressing necessity. One of the consequences of this situation is the need for enforcing security (e.g., access control) and privacy (e.g., data protection) policies to fulfil increasingly complex security and privacy requirements. To make policy enforcement even more difficult, providers offering Cloud and Edge services are known to pose a threat to data privacy by actively collecting and analysing such data [2] (this is known as "Honest but Curious" providers). As a result, data must be encrypted throughout their lifecycle so that only authorised devices can actually decrypt the data. Unfortunately, cryptography is still a challenging task given the low resources deployed at the Edge (e.g., IoT devices with limited memory and computational power), and this makes the enforcement of expressive policies a difficult task.

Therefore, there is an urgent need to design innovative solutions to enforce rich security and privacy policies in resource-constrained distributed systems involving "Honest but Curious" Cloud and Edge providers. Alternative enforcement mechanisms should be deployed by considering the characteristics of the various components integrated into a Cloud-Edge system (e.g., centralized policies for the Cloud or lightweight cryptography for IoT devices). The enforcement of policies should be distributed to minimize the effort based on the available resources while still guaranteeing the completeness and efficacy of the policy at the logical level. The design of these solutions must consider usability and efficiency by adopting current best practices and using tools that assist the correct implementation.

The research work to be conducted in the thesis aims to make significant contributions to developing methodologies, techniques, and tools for the security and privacy of data in Edge-enabled applications. The activities will include:

* Analysis of the state-of-the-art solutions in securing and protecting data of Edge-enabled applications;
* Design of an innovative solution allowing to efficiently distribute the enforcement of policies in Edge-enabled applications;
* Implementation of such a solution adopting the current best practices to guarantee efficiency and frictionless user experience.

References:

[1] https://gdpr.eu/

[2] E. Ramirez, J. Brill, M.K. Ohlhausen, J.D. Wright, and T. McSweeny, "Data brokers: A call for transparency and accountability". In Data brokers: A call for transparency and accountability, pages 1–101. CreateSpace Independent Publishing Platform, January 2014.

A large number of data breaches are caused by passwords, that represent the weakest link in the authentication chain since they are usually pretty simple to guess and reused across many different online accounts. Therefore, authentication strategies are moving towards more secure solutions as Multi-Factor Authentication (MFA) [1] and Passwordless Authentication [2] (e.g., using push notification or QR codes). Electronic identity documents (also called eID documents) are a significant example: they can be used alone without requiring a password-based authentication and are considered as a MFA solution since they attest both a possession (the card itself) and a knowledge factor (the related PIN code). Moreover, such documents can also provide a certain degree of assurance on their owners’ identity, thus significantly simplifying the link between digital and real identities in sensitive contexts. Due to these countless features, eID documents are increasingly involved not only in authentication, but also in the so-called “enrollment” – an online procedure adopted by many banks or service providers to let customers subscribe to a service or sign a contract while having a certain degree of assurance on their real identities [3]. The increasing adoption of these protocols and the sensitive contexts in which they are used require the development of automated security analysis techniques to increase the trust of all stakeholders, from end-users to system administrators while striking a good trade-off among security, privacy, usability, compliance, and efficiency.

The research work to be conducted in the thesis aims to make significant contributions to developing methodologies, techniques and tools to both secure-by-design approaches and the security assessment of Identity Management solutions, including:
- The design of enrollment and authentication protocols relying on eID documents and/or open standards (e.g., OIDC and FIDO2) with particular attention to regulatory issues e.g., PSD2 and Know Your Customer best practices in the financial sectors.
- The automated security analysis of authentication and authorization protocols by exploiting state-of-the-art formal techniques to detect violations of security properties along the lines of e.g., the approach described in [4].
- The automated assessment of risks to which authentication and authorization protocols are exposed by extending existing tools such as MuFASA [5].

References:
[1] National Institute of Standards and Technology. Digital Identity Guidelines (Special Publication 800-63). Available at: https://pages.nist.gov/800-63-3/

[2] World Economic Forum. Passwordless Authentication: The next breakthrough in secure digital transformation. Available at: http://www3.weforum.org/docs/WEF_Passwordless_Authentication.pdf

[3] Silvio Ranise, Giada Sciarretta and Alessandro Tomasi. Enroll, and authentication will follow: eID-based enrollment for a customized, secure, and frictionless authentication experience. In: 12th International Symposium on Foundations & Practice of Security (FPS 2019). DOI: 10.1007/978-3-030-45371-8_10

[4] Giada Sciarretta, Roberto Carbone, Silvio Ranise, Luca Viganò. Design, Formal Specification and Analysis of Multi-Factor Authentication Solutions with a Single Sign-On Experience. In: Principles of Security and Trust (POST 2018). DOI: 10.1007/978-3-319-89722-6_8

[5] Federico Sinigaglia, Roberto Carbone, Gabriele Costa, Silvio Ranise. MuFASA: A Tool for High-level Specification and Analysis of Multi-factor Authentication Protocols. In: Emerging Technologies for Authorization and Authentication ([email protected] 2019). DOI: 10.1007/978-3-030-39749-4_9

Space and avionics systems are reaching an unprecedented degree of complexity. The process of safety analysis attempts to characterize the likelihood of faults and failures, and to assess the effectiveness of the adopted mitigation measures. Unfortunately, traditional techniques are becoming ineffective, unable to deal with large-scale systems. This thesis will investigate novel methods for safety analysis, based on the adoption of formal models of system and software (nominal and faulty) behaviors. Particularly interesting are the analysis of timing aspects in the propagation of multiple faults to failures and errors, the ability to explain the causality of propagation, and the definition of techniques for on-the-fly fault detection, isolation and recovery policies.

The proliferation of digital and human-centric systems, spurred by the introduction of 5G communications, Internet of Things (IoT) and Cloud Computing technologies, is posing unprecedented challenges in terms of cybersecurity, with plenty of software and hardware vulnerabilities that attackers are eager to find and exploit. In this context, Artificial Intelligence (AI) is offering powerful instruments (e.g. Deep Learning) to effectively detect attack patterns and discover threats.
With this PhD scholarship, we are seeking candidates that will carry out research and development activities aimed at defining and implementing novel AI-based methods and platforms to secure modern networked systems.
We are therefore encouraging applications from candidates with solid knowledge of the networking, security and AI basics, skilled in programming, eager to work within a vibrant team and on cutting-edge technologies. Successful candidates will also have the opportunity to develop relevant expertise on Software Defined Networks (SDN) and programmable data planes (extended Berkeley Packet Filter - eBPF, P4), as well as on cloud-native technologies (microservices and serverless architectures, linux containers, container orchestration).

Current approaches in statistical relational learning are based on undirected graphical models such as Markov Logic Networks. State of the art algorithms for statistical inference cover the Maximum Likelihood (ML) and Maximum a Posteriori (MAP) tasks, but not so much attention has been devoted to Bayesian Inference. Due to the high complexity of the models that can be generated, statistical inference is approximated using sampling methods. Recently, we proposed a study about Bayesian Inference in hybrid graphical models (i.e., models composed of discrete and continuous random variables); the advantage of Bayesian inference is that, it’s a truly statistical inference and it is very robust to overfitting training data. We design a variational method to solve the “exact inference”. However, to perform Bayesian inference, combinatorial problems on the discrete variables must be solved in a more efficient way, and this is still an open problem. The objective of this thesis, is to extend such proposals and to make scalable.

Evidence-based research is showing positive effects of virtual reality interventions on patients with cognitive decline, in terms of both stimulation and relaxation, targeting key outcome variables such as cognition and emotion. Semi-immersive technology is a promising and effective tool to use with older adults affected by mild cognitive decline or dementia, for delivering interventions able to improve cognitive and routine functions, stimulating patients’ brains in rich multisensory settings. However, recent findings show also the need for developing comprehensive guidelines to develop and implement safe, effective VR interventions for people with cognitive decline. The aim of the PhD project is to deploy co-design methods and guidelines for the design and evaluation of semi-immersive VR environments for patients with cognitive decline in collaboration with patients’ associations and communities. The ideal candidate will be strongly motivated in developing design and evaluation skills in the field of virtual reality applications for healthcare.

Conversational agents are key components of artificial intelligence solutions for the delivery of behavior change interventions in healthcare settings. They provide user-friendly, intuitive interfaces based on dialog-based interaction for supporting the promotion of healthy lifestyles or patients’ compliance with treatment in chronic conditions. The aim of the PhD project is to investigate and deploy effective methodologies for the design and testing of conversational agents, based on deep learning techniques, to deliver virtual coaching interventions in the healthcare domain. The ideal candidate will be strongly interested in conducting interdisciplinary work in the area of AI for BIC (Behavioral Intervention Technologies).

Althought 5G has just arrived, the research towards 6G mobile networks has already started. 5G paved the way towards a connected world where multiple verticals (e.g., automotive, industry, and health), each characterised by different performance targets in terms of bitrate, latency, and reliability, can coexist on the same infrastructure. 6G networks will push this paradigm even futher and will require a paradigm shift in the way mobile networks are deployed and operated.

With this fully funded PhD position we are looking for a candidate willing to work on cutting edge research in the field of 6G mobile with a particular focus on data-driven approaches for network management and operation (slicing, edge/fog computing, isolation, resiliency, etc).

The successful candidate has obtained a master's degree with excellent marks in computer science, is proficient in networking and programming, has an affinity for algorithm design and artificial intelligence, and enjoys working in a multi-disciplinary project. In particular, evidence of hands-on experience with software-defined networking for RANs and open-source LTE/5G stacks is considered to be an advantage.

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in radar images.
The candidate will be requested to deal with images acquired from active systems including Synthetic Aperture Radar (SAR) images acquired from Earth Observation satellite missions, and sub-surface radar sounding data from airborne Earth Observation missions and satellite planetary exploration missions. The latter activity is developed in the framework of the Radar for Icy Moons Exploration (RIME) payload on board of European Space Agency (ESA) JUpiter ICy moons Explorer (JUICE) and Sub-surface Radar Souder (SRS) payload under development for the European Space Agency (ESA) Europe's Revolutionary Mission to Venus (EnVision).
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, image/signal processing, statistic/remote sensing/radar.

We are looking for candidates willing to develop novel methodologies based on machine learning, deep learning pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in multi-/hyper-temporal remote sensing images.
The candidate will be requested to deal with both multi-/hyper-spectral images acquired by passive satellite sensors and Synthetic Aperture Radar (SAR) images acquired from active systems for Earth Observation. The goal is to design novel methods able to use temporal correlation to model landcover behavior, changes and trends for a better understanding of phenomena over time and of climate change.
Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are:
• master degree in Electrical Engineering, Communication Engineering, Computer Science, Mathematics or equivalents;
• knowledge in pattern recognition, image/signal processing, statistic/remote sensing, passive/active sensors.

This PhD project has the ambition to explore the fusion of multiple modalities and the design of novel cross-modal deep neural network architectures to analyze and generate behaviors and social interactions in multiple settings (e.g. urban scenarios, workplace settings, etc.). Special emphasis will be given to exploit synthetically generated data within GAN architectures. The ideal candidate will be strongly motivated in developing skills in machine learning with a special focus on deep learning, in computer vision and multimodal approaches as well as in human behavior understanding. The project will be supervised by Bruno Lepri (FBK).

The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In particular, current hate speech detection systems do not perform well on under-resource languages, are not able to generalise well across different platforms, and fail to integrate contextual information (e.g. network structure, discourse context, links to external media). We are therefore looking for candidates with strong interest in hate speech detection using deep learning techniques that would contribute to the development of novel approaches for robust hate speech detection designed to work in multilingual settings with small or no language-specific training data (zero-shot learning).

We are looking for a candidate willing to embark a challenging activity focused on the development of innovative Microsystems-based Radio Frequency RF-MEMS passive components and networks for next generation of telecommunications, wireless and radio systems and applications, like 5G and the Internet of Things (IoT).
Capitalising on the fully in-house RF-MEMS technology, the candidate will have the opportunity to focus on different stages of prototypes development, from the elaboration of novel RF-MEMS design concepts, to the multi-physical simulation, modelling, fabrication, experimental testing of physical samples and integration.

Embedded systems are a fundamental component of our world. Their ubiquitous adoption and ever-increasing complexity makes the task of verifying their correctness both extremely challenging and extremely important. Techniques based on formal methods and automated theorem proving (particularly in the form of Satisfiability Modulo Theories - SMT) are very appealing in this context, promising to deliver both a higher level of confidence than traditional techniques and a high degree of automation. The objective of this PhD research is that of advancing the state of the art in the application of SMT-based formal methods to parameterized systems -- systems consisting of an unbounded number of components/processes -- which naturally arise in many safety-critical domains. The candidate will work on both theoretical aspects of the problem, as well as its practical applications in relevant case studies, drawn from the domains of railways, avionics and aerospace.

The need to translate the audio from one language into a text in a target language has dramatically increased in the last few years with the growth of audiovisual content freely available on the Web. Current speech translation (ST) systems need to be able to serve different applications working in various scenarios and to satisfy several factors coming from the market (e.g. specific length of the output, adaptation to different domains, real-time processing) or present in the audio (e.g. background noise or strong accent of the speaker). The objective of this PhD is to advance the state of the art in speech translation to make ST flexible and robust to these and other factors. Candidates should have a strong curiosity to solve problems in natural language processing and have a background in deep learning and maths, as well as excellent programming skills in Python. They will work both on theoretical aspects of the problem, and on their practical application in relevant case studies driven by ongoing projects where the MT unit is involved. Applicants are invited to contact us ([email protected] and [email protected]) in advance for preliminary interviews.

The Internet of Things paradigm is driving innovation in several application domains, such as digital industries, healthcare, smart cities, energy, retail, agriculture, etc. IoT architectures have been evolving from an initial centralized approach towards more distributed and edge-centric ones, where everyday-more-autonomous IoT devices are able to interact in a decentralized and effective way. However, the huge variability of requirements that an IoT platform needs to cope with and fulfill to serve such diverse application domains and scenarios suggests that a holistic architectural approach cannot be the solution. Indeed, depending on the specific application context, different challenges emerge, while often conflicting requirements need to be addressed meeting the best trade-offs between energy management and conservation, cognitive capabilities deployed on autonomous devices, latency and bandwidth over low-power communication protocols, timeliness of data exchange, reactivity and reliability in wide area networks.
The objective of this PhD is to study, analyze and identify novel energy-aware architectural approaches capable to balance the use of available energy resources within constrained IoT devices with embedded computing capabilities and possible strategies to overcome the existing technological limitations by using blended strategies, such as combination of energy harvesting technologies, computation offloading from the edge to the cloud, adaptive data exchange paradigms, and distributed artificial intelligence along the cloud-to-thing continuum.

The Phd project can evolve along either one of the two research lines described below.

Research line 1: Personal agents for mindfulness-based interventions in healthcare. In the last decade there has been a growing interest for mindfulness interventions delivered by means of mobile applications and personal assistants to support self-care of patients, including those coping with chronic conditions. Despite the fact that the validity of mindful practices has been proved repeatedly by previous research, the design of effective behavioral intervention technologies for mindfulness coaching and practice remains a challenge. The aim of the PhD project is to investigate key features of smart coaching solutions for mindful-based interventions that are engaging to use by patients and produce effective outcomes from a clinical perspective. The ideal candidate will be strongly motivated in developing design skills in the field of behavioral intervention technologies and conversational agents for applications in healthcare.

Research line 2: Computational creativity is a sub-field of artificial intelligence concerned with the development of programs that can produce creative output;
in particular, many of these programs deal with linguistic creativity.
Recent years have witnessed a growing interest in computational linguistic creativity, a research field at the boundary between many disciplines including
natural language processing, linguistics, psychology, and of course cognitive sciences. Even though the state-of-the-art in this field is advancing a bit in the last years, real-world applications of computational linguistic creativity are still uncommon. Particular focus of the PhD will be put in computational treatment of figurative, emotional, witty language considering also in multimodal (e.g. visual) context.

Responsible for the project: Silvia Gabrielli / [email protected] (for line 1) and Carlo Strapparava [email protected] (for line 2)

With the increasing availability of powerful mobile and ubiquitous computing technologies, motivational and behavior change technologies, traditionally applied in the health science domain, have become commonplace and have expanded to cover several other domains. In particular, game-based motivational systems (i.e., serious games, games with a purpose, gamification) are emerging as an effective tool to induce a positive change in human behaviors in relation to environmental challenges, social engagement, safety, productivity and learning. Games introduce goals, interaction, feedback, problem solving, competition, narrative, and fun learning environments, motivational affordances that can increase end-user engagement and induce a voluntary behavior change. Although several research studies proved the validity of game-based motivational systems, their effectiveness has often been context specific and varied among individuals. A promising and ever-growing research field concerns the investigation of user-centered, personalized and adaptive gamified dynamics, tailored to specific users and contexts. In the context of games, adaptivity describes the automatic adaptation of game elements, i.e., of content, user interfaces, game mechanics, game difficulty, etc., to customize or personalize the interactive experience. Personalization and adaptivity can promote motivated usage, increased user acceptance, and user identification in serious games.

The aim of the PhD project is to investigate key features of adaptive personalized motivational game-based systems addressing one or more of the following research challenges: analyse the relationship between the mechanics and their effects on different individuals to react accordingly; investigate adaptive game dynamics in multi-player settings, finding the right balance between individual (micro) level and community (macro) level personalization; design of meaningful adaptive gamified reinforcement strategy to sustain players’ long-term motivation (e.g., churn prediction and generation of dynamic personalized re-engagement incentives).

The PhD project focuses on the cognitive basis of computational thinking for the design of digital technologies in the field of Internet-Of-Things aimed at improving learning and education. In particular, the research explores digital tools that can be manipulated and adapted by teachers and children themselves (the so-called "End-User Programming").

Research activity conducted during the Ph.D. will focus on the development of computational tools aimed at providing insight into factors influencing the spatio-temporal spread of infectious disease in humans and reliable estimates of the expected impact of public health interventions. Envisioned approaches range from the study of mechanistic models mimicking the transmission processes underlying outbreak, epidemic, and pandemic situations to the use of statistical inference applied to epidemiological data.

A clinical case is a statement of a clinical practice, presenting the reason for a clinical visit, the description of physical exams, and the assessment of the patient’s situation. Clinical cases (e.g. discharge summaries, clinical cases published in journals, and clinical cases from medical training resources) provide a very valuable source of information for data-driven technologies aiming at predicting clinical outcomes and patient behaviors.

This three-year PhD offers a unique context of a collaboration between FBK, specifically the NLP group, and a Swiss multinational healthcare company, worldwide leader in biomedical research.

The research will focus on a number of application oriented tasks, including automatic recognition of clinical entities (e.g. pathologies, symptoms, procedures, and body parts, according to standard clinical taxonomies such as ICD-9, ICD-10 and SNOMED-CT); detection of temporal information (i.e. events, time expressions and temporal relations, according to the THYME TimeML standard), and factuality information (e.g. event factuality values, assessment of the effect of negation, uncertainty and hedge expressions).

Italian will be the major language of clinical cases, although technologies will be experimented on other languages.

The goal of the PhD is both to advance the state of the art for clinical case analysis for the Italian language, and to deliver prototype applications, which can be further made operative in real settings (e.g. hospitals).
The candidate will have the unique opportunity to explore different domains (Natural Language Processing, Machine Learning, Health & Well-Being) being directly coached by very experienced teammates.

The involved PhD will work in an international environment, collaborating with a healthcare company, with worldwide presence. The candidate will work both at FBK (Trento) and at the abovementioned company’s premises (both in Italy and abroad).

The assurance of safety requirements is fundamental in many safety-critical domains. In particular, in the automotive domain, the increased automation and connectivity of embedded systems demand for more rigorous analysis of their functional safety. The advances in sensor and actuator technologies enable new solutions to increase the safety and reliability of systems. At the same time, their complexity makes the assurance of such properties more and more challenging.

This thesis will concentrate on developing new formal methods for the design of safety solutions in automated systems. It will investigate advances with respect to the state of the art on formal methods about the specification and verification of safety contracts, the diagnosis and prognosis of their failure, and the related diagnosability problem. The validation of the methods will be performed with case studies developed in automotive domain and based on real-world solutions developed by Bosch.

The correct specification of requirements is a cornerstone in the design of safety-critical systems and in the assurance of safety requirements. Errors and ambiguities in the requirements are considered a fundamental problem by many industries across application domains. Rigorous techniques based on formal methods have been shown to be promising to address this problem. However, these techniques are difficult to take up at the industrial level. There is still a large gap between the information requirements and their formal counterpart. Each domain has specific ways to specify the requirements that depend also on the kind of system or component under design. Moreover, the semantic interpretation is also very dependent on the domain, which typically has some assumptions and background knowledge that are very expensive to formalize.

This thesis will focus on the development of a new open-source framework based on formal methods for the formalization and validation of domain-specific requirements. The work will support the creation of domain-specific control natural languages for requirements. Moreover, it will provide means to specify the domain-specific background knowledge and to exploit for tailored analysis engine. The framework will be validated with case studies provided by Intecs, which with its experience in diversified domains (railways, aerospace, IoT, etc) will be a suitable environment for this purpose.

Digital technologies play an ever increasing role in all aspects of human society; this induces a wide range of changes, collectively referred to as Digital Transformation, that, far from being only technological, also cover cultural, organizational, social, managerial aspects of our life.
Artificial Intelligence is a key technology for digital transformation, thanks to its capability to extract information and knowledge from data; this requires the capability to open, analyze and exploit all data available on a given phenomenon, data that are often highly heterogeneous, scattered, and coming from different sources (e.g. open, sensor, free, closed, linked data). This thesis will concentrate on developing a data-driven computational framework, based on AI approaches, able to perform data analysis and prediction in the setting just described. The framework will be developed in the scope of the Digital Hub, a digital platform jointly developed by Dedagroup and Fondazione Bruno Kessler to address digital transformation in different application domains, including Public Administration, Digital Finance, Digital Industry. The validation of the framework will be performed addressing problems in these application domains, by exploiting the data sets and services integrated in the Digital Hub.

This project will focus on data science and deep learning to quantify and model observations made within experiments of particle physics (ATLAS at the LHC, future colliders) and in space (HEPD-01/02, Aladino). The project will develop novel analytics tools for event building software, including feature tagging, consequent event selection and particle identification.

The candidate will perform research on highly correlated and interdependent networks, providing a model for many empirical complex systems, natural and artificial. The underlying framework is based on statistical physics at and out of equilibrium, and nonlinear dynamics. The candidate will develop the theoretical and computational ground for the analysis of collective phenomena emerging in such systems, with application to biological, social and urban networks.

The PhD grant has the goal of developing methodologies, based on statistical physics, for modeling complex systems such as socio-technical, urban, or economic systems. The developed multilayers models will be adopted to investigate and explain the complex characteristics of the observed socio-economic phenomena with the goal of identifying a minimal set of microscopic mechanisms able to reproduce collective phenomena on meso- or macroscopic scales.

Conversational agents are supposed to help users to accomplish common tasks, like booking a restaurant, buying a product, give commands while driving a car, or suggesting correct life styles.
In the last years several methods based on deep learning architectures have been developed to build conversational agents that learn their behavior directly from data (i.e. dialogues). Although this research line is very promising, several challenges are still open, including (i) how to incorporate domain knowledge into end-to-end architectures; (ii) how to reproduce human-like conversational behaviors, particularly those related to collaboration, and (iii) how to address the scarcity of annotated dialogues for many application domains and languages.
This PhD grant aims at advancing the state-of-the-art in selected topics in data-driven conversational agents, building on top of current research and projects carried on at the NLP group at FBK.

Process mining is a family of methods that aim at analyzing business processes based on their observed behavior recorded in event logs. Sometimes, however, together with data related to the observed behaviour, additional knowledge about the process execution is also available (e.g., knowledge provided by domain experts, knowledge related to rules that have to be observed). This knowledge can be leveraged in order to improve the accuracy of the results of state-of-the-art process mining techniques, e.g., to discover more accurate process models or to provide more accurate prediction.
Within this PhD program, the candidate will work towards combining the information gathered from recorded event logs and additional knowledge in the context of different process mining scenarios, such as process discovery and predictive process monitoring. Achieving this task requires dealing with several challenges as, for example, the capability to envision new algorithms able to combine data and knowledge and the capability to implement scalable solutions.
The work will put together theoretical and methodological aspects, ranging from the capability to identify the correct representation for a complex problem up to the development of process mining algorithms and tools.

The ability of modeling, understanding and predicting human behaviors and social interactions is fundamental for computational social science and has a range of relevant applications for individuals, companies, and societies at large. In this project, the goal is merging approaches from network science and machine learning and using data on mobility routines (e.g. GPS and other mobile phone data), face-to-face interactions and purchase behaviors (e.g. credit card transactions) in order to develop methods for quantify daily habits, individual dispositions and traits, and behavioral changes. A special attention will be given to the changes on daily human behaviors due to the emergence and spread of the Covid-19 pandemic. The Ph.D. project will be conducted within the FBK MobS research unit but with collaborations with several international groups.

SPAD and SiPM mixed-signal interfaces for LiDAR pose several challenges in terms of low noise, low power and speed. Clever architectures and optimized designs are necessary to overcome the current limitations: this PhD position aims at investigating the next-generation interfaces for SPAD and SiPM, focused on the distance ranging application but also applicable to the readout frontends for particle physics and medical instrumentation. The activity spans from the modeling aspect, to the readout IC design, simulation and characterization, to the system integration, requiring electronics design skills but also knowledge of optics and discrete electronics.

Automatic semantic segmentation of images and 3D data is becoming a very prominent research field with many interesting and reliable solutions already available. To this end, machine learning approaches are fundamental to automatize the processing and to couple geometric data with semantic labels. The aim of the PhD is to advance the state-of-the-art in semantic photogrammetry (including SLAM and MVS) applied to industrial, heritage and territorial applications where real-time is requested. Within the real-time image processing we want to embed classification solutions in order to offer semantic 3D data useful for e.g. inspection, quality control, monitoring, etc.

Automated Planning is the problem of synthesizing courses of actions guaranteed to achieve a desired objective, given a formal model of the system being controlled. A class of problems particularly interesting for applications is temporal planning (also called planning and scheduling) where the discrete decisions of "what to do" are coupled with the problem of scheduling (deciding "when to do"). Unfortunately, planning and scheduling techniques suffer from scalability issues and are often unable to cope with the complexity of real-word scenarios, despite the plethora of approaches available in the literature.
Recently, efforts such as Deepmind AlphaGO and OpenAI Five hit the headlines, with groundbreaking advancements in the field of reinforcement learning. These techniques are able to automatically learn policies to decide what to do in order to achieve a desired goal. However, they offer no formal guarantee and are not model-based. The research objective of this PhD scholarship is to investigate techniques that combine the formal guarantees offered by automated planning and scheduling with the performance and self-improving capabilities offered by recent advances in deep reinforcement learning to construct self-adaptive planners that can improve over time their performance on specific application scenarios.

Silicon Photomultipliers (SiPMs) are silicon photodetectors with internal signal amplification obtained by means of the impact ionization mechanism, operating in the so-called Geiger mode and achieving a gain in the order of a few millions per a single, photo-generated carrier. They are extremely fast and sensitive detector, having demonstrated the capability of detecting light down to the single photon level and with a Single Photon Time Resolution (SPTR) of 20 ps FWHM.
Thanks to these exceptional characteristics, SiPMs are currently of great interest in several scientific and industrial applications. They are being considered for the detection of faint light signals in the large majority of big scientific experiments of the next generation, ranging from the HL-LHC upgrade to future Dark Matter and Neutrino experiments. For these experiments, SiPM technology needs to be developed further, to obtain, among other features, enhanced radiation hardness, effective operation at cryogenic temperatures and sensitivity to extremely low-wavelength photons down to 128 nm, for direct detection of LAr scintillation light. By overcoming such difficult challenges, SiPMs, will constitute an enabling technology for scientific research over the next decade.
In this context, Fondazione Bruno Kessler (FBK, Trento) is at the forefront of developments. FBK is currently collaborating with CERN and several other research institutions for the development of rad-hard SiPMs, which are required to withstand radiation doses in excess of 1e14 neq/cm2. Considering cryogenic operation of SiPMs, R&D carried out by FBK for experiments such as DarkSide-20k (http://darkside.lngs.infn.it/), nEXO (https://nexo.llnl.gov/) and DUNE (https://www.dunescience.org/) has already achieved outstanding results, such as almost noiseless detector operation at 87 K (LAr temperature). On the other hand, research is still ongoing to improve detector characteristics cold temperatures, such as reducing afterpulsing, output capacitance and increasing photon detection efficiency (PDE).
The PhD candidate will work in the IRIS research unit of FBK (https://iris.fbk.eu/), which is composed of approximately 20 people, mostly researchers and PhD students, working on SiPM, SPAD and CMOS image sensors development. Within IRIS, his/her research activity will be focused on the study and development of SiPMs built in custom technology in FBK clean-room. The PhD candidate will be inserted in a team of researchers developing word-class sensor technologies, with the purpose of becoming proficient in the field of instrumentation and, in particular, of silicon sensors and readout techniques. He/She will learn how to characterize and optimize the components of a photon or radiation detection system, taking into account the requirements of different scientific and industrial applications.
The PhD candidate will dedicate his/her research to one or a few scientific projects, selected together with his/her supervisor among the ones carried out by FBK and characterized by uniform technical challenges. At the beginning of the three-year studies, he/she will be focused on understanding the SiPM working principle and its main characteristics and on becoming expert in some of the different, advanced characterization setups and techniques employed at FBK. They include: semiconductor analyzers, high-speed digitizing oscilloscopes, ultra-fast laser sources, thermostatic chambers, a cryostat, instrument programming in Labview, digital data processing, data analysis, development of custom, front-end electronics, scintillating materials and radioactive gamma-ray sources. An important part of his/her activities will be constituted by the advanced characterization of different SiPM process and layout splits, which are usually included in R&D runs fabricated at FBK. Such activity will be aimed at understanding the features of the device, studying the different phenomena involved, evaluating the effectiveness of the technical solution implemented and proposing further developments of the sensor. After acquiring sufficient experience, towards the end of the PhD studies, it will be also possible for him/her to contribute to the design of new SiPM sensors.
At the end of the PhD studies, the PhD candidate is expected to become proficient in the field of sensors, instrumentation and characterization setups. He/She will be able to start a career either in the scientific field, for example joining a large scientific collaboration, in which his/her competences are usually highly valued.
For any further question or curiosity, please contact Dr. Alberto Gola, [email protected]

Silicon Photomultipliers (SiPMs) are silicon photodetectors with internal signal amplification obtained by means of the impact ionization mechanism, operating in the so-called Geiger mode and achieving a gain in the order of a few millions per a single, photo-generated carrier. They are extremely fast and sensitive detector, having demonstrated the capability of detecting light down to the single photon level and with a Single Photon Time Resolution (SPTR) of 20 ps FWHM.
Thanks to these exceptional characteristics, SiPMs are currently of great interest in several scientific and industrial applications. They are being considered for the detection of faint light signals in the large majority of big scientific experiments of the next generation, ranging from the HL-LHC upgrade to future Dark Matter and Neutrino experiments. On the other hand, SiPMs have also very important industrial applications, with great potential to improve health and several aspects of people’s lives. The most traditional industrial application is the use of SiPMs to build next-generation, Time-of-Flight Positron Emission Tomography machines (ToF-PET), which are used in neurology, cardiology, oncology, drug development, and in a number of other medical imaging use cases. A second, extremely interesting SiPM application is LIDAR for automotive. Indeed, SiPMs are considered one of the most promising technology solutions to enable LIDAR sensors that will provide 3D vision to next-generation, autonomous driving systems (level 4 and above).
In this context, Fondazione Bruno Kessler (FBK, Trento) is at the forefront of developments. FBK is currently collaborating with CERN and several other research institutions for the development of rad-hard SiPMs, which are required to withstand radiation doses in excess of 1e14 neq/cm², and of cryogenic SiPMs, for experiments such as: DarkSide-20k (http://darkside.lngs.infn.it/), nEXO (https://nexo.llnl.gov/) and DUNE (https://www.dunescience.org/). Collaboration with companies is also very active, with ongoing projects related to development of next-generation SiPMs for both ToF-PET and LIDAR.
The PhD candidate will work in the IRIS research unit of FBK (https://iris.fbk.eu/), which is composed of approximately 20 people, mostly researchers and PhD students, working on SiPM, SPAD and CMOS image sensors development. Within IRIS, his/her research activity will be focused on the study and development of SiPMs built in custom technology in FBK clean-room, with special attention dedicated to the interaction between sensor and front end, to advanced 3D integrated and BSI SiPM structures. In this context, the partnership between FBK, which develops SiPMs, and Politecnico di Torino and INFN, sezione di Torino, which are expert in CMOS ASIC design, is strategic. The PhD candidate will be inserted in a team of researchers developing word-class sensor technologies, with the purpose of becoming proficient in the field of instrumentation and, in particular, of silicon sensors and readout techniques. He/She will learn how to characterize and optimize the components of a photon or radiation detection system, taking into account the requirements of different scientific and industrial applications.
The PhD candidate will dedicate his/her research to one or a few scientific projects, selected together with his/her supervisor among the ones carried out by FBK and characterized by uniform technical challenges. At the beginning of the three-year studies, he/she will be focused on understanding the SiPM working principle and its main characteristics and on becoming expert in some of the different, advanced characterization setups and techniques employed at FBK. They include: semiconductor analyzers, high-speed digitizing oscilloscopes, ultra-fast laser sources, thermostatic chambers, a cryostat, instrument programming in Labview, digital data processing, data analysis, development of custom, front-end electronics, scintillating materials and radioactive gamma-ray sources. An important part of his/her activities will be constituted by the advanced characterization of different SiPM process and layout splits, which are usually included in R&D runs fabricated at FBK. Such activity will be aimed at understanding the features of the device, studying the different phenomena involved, evaluating the effectiveness of the technical solution implemented and proposing further developments of the sensor. After acquiring sufficient experience, towards the end of the PhD studies, it will be also possible for him/her to contribute to the design of new SiPM sensors.
At the end of the PhD studies, the PhD candidate is expected to become proficient in the field of sensors, instrumentation and characterization setups. He/She will be able to start a career either in the scientific field, for example joining a large scientific collaboration, in which his/her competences are usually highly valued.
For any further question or curiosity, please contact Dr. Alberto Gola, [email protected]