Old Calls

2021

  • University of Trento

  • PhD Programme in Information Engineering and Computer Science

  • Neural Dialogue Models for fighting misinformation and hate speech
    Contacts: Marco Guerini
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Testing for complex parametric systems
    Contacts: Angelo Susi
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Formal verification of complex cyberphysical systems
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Formal verification of hybrid system models for control software
    Contacts: Stefano Tonetta
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Self-Adaptive Automated Planning and Scheduling via Combination with Reinforcement Learning
    Contacts: Andrea Micheli
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Learning-based 3D scene understanding
    Contacts: Fabio Poiesi
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Multi-Access Edge Computing for Beyond 5G Networks
    Contacts: Cristina Costa
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Remote sensing image time series analysis for climate change
    Contacts: Francesca Bovolo
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Fairness and explainable methods for machine learning and deep learning algorithms
    Contacts: Bruno Lepri
    Deadline: April 15, 2021 Expired
    Abstract:

    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/).

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  • End-2-End AI technologies for the semantic interpretation of audio and speech data
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Persona Based neural models for Opinionated Dialogues
    Contacts: Marco Guerini
    Deadline: April 15, 2021 Expired
    Abstract:

    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.

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  • Flexibility and Robustness in Speech Translation
    Contacts: Marco Turchi
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • AI at the edge: end-to-end neural networks for audio processing on IoT devices
    Contacts: Alessio Brutti
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • Advanced methodologies for radar and radar sounder image processing
    Contacts: Francesca Bovolo
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • Deep continual learning under scarce supervision
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • Engineering Game-based Motivational Digital System for Personalized and Cooperative Learning
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • Computational models for understanding and changing human behaviors
    Contacts: Bruno Lepri
    Deadline: August 31, 2021 Expired
    Abstract:

    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).

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  • Distributed embedded AI for energy-efficient smart sensing in IoT
    Deadline: August 31, 2021 Expired
    Abstract:

    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.

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  • Multi-objective optimization methods to support one-click deployments of EdgeAI application flows
    Contacts: Fabio Antonelli
    Deadline: August 31, 2021 Expired
    Abstract:

    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.).

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  • Doctoral Programme in Physics

  • Deep Learning for Time-transient phenomena in the ionosphere and correlation with seismo-induced events
    Deadline: May 24, 2021 Expired
    Abstract:

    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.

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  • Gluon Saturation at the Electron-Ion Collider
    Deadline: May 24, 2021 Expired
    Abstract:

    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).

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  • Optimizing Quantum Simulations for Trapped-Ion qubits
    Contacts: Daniele Binosi
    Deadline: May 24, 2021 Expired
    Abstract:

    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.

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  • Doctoral School in Cognitive and Brain Sciences

  • Machine Learning for Brain Connectivity in Clinical Neuroscience
    Deadline: May 27, 2021 Expired
    Abstract:

    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).

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  • Doctoral School in Mathematics

  • Analytical, stochastic, and applicative aspects of Deep Neural Networks
    Contacts: Giuseppe Jurmann
    Deadline: May 31, 2021 Expired
    Abstract:

    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;

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  • Evaluation of interventions against infectious diseases
    Contacts: Giorgio Guzzetta
    Deadline: May 31, 2021 Expired
    Abstract:

    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.

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  • Data-driven approaches in epidemiological modeling
    Contacts: Piero Poletti
    Deadline: May 31, 2021 Expired
    Abstract:

    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.

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  • Doctoral Programme in Biomolecular Sciences

  • XAI in integrative bioimaging&omics
    Contacts: Giuseppe Jurman
    Deadline: July 22, 2021 Expired
    Abstract:

    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.

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    Attachments: application
  • Doctoral Programme in Civil, Environmental and Mechanical Engineering

  • Precise positioning in photogrammetric application / Photogrammetry aided by positioning techniques
    Contacts: Fabio Remondino
    Deadline: July 26, 2021 Expired
    Abstract:

    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.

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    Attachments: application
  • Development and validation of multiphysics-multiscale tools for redox flow battery design
    Deadline: July 26, 2021 Expired
    Abstract:

    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.

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    Attachments: application
  • Doctoral Course in Cognitive Science

  • Educational technologies
    Deadline: July 27, 2021 Expired
    Abstract:

    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.

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  • Olfactory information extraction and analysis
    Contacts: Sara Tonelli
    Deadline: July 27, 2021 Expired
    Abstract:

    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.

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  • Personal agents for healthy coping interventions in healthcare
    Contacts: Silvia Gabrielli
    Deadline: July 27, 2021 Expired
    Abstract:

    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.

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  • Game-based motivational technologies for personalized collaborative learning
    Deadline: July 27, 2021 Expired
    Abstract:

    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.

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  • Doctorate Program in Industrial Innovation

  • Investigation of the direct ammonia synthesis and its utilization in reversible HT cells
    This scholarship is granted by a collaboration with SNAM S.p.A.
    Deadline: August 25, 2021 Expired
    Abstract:

    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.

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    Attachments: SNAM S.p.A.
  • Interpretation of very large-scale conversational data
    This PhD Executive position is granted by a collaboration with SoftJam S.p.A.
    Deadline: August 25, 2021 Expired
    Abstract:

    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.

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    Attachments: SoftJam S.p.A.
  • Using Satellite Imagery and Deep Learning for Understanding Socio-Economic Development
    This scholarship is granted by a collaboration with MindEarth
    Deadline: August 25, 2021 Expired
    Abstract:

    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).

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    Attachments: MindEarth
  • Deep Learning for Understanding Visual Scenes
    This scholarship is granted by a collaboration with MindEarth
    Deadline: August 25, 2021 Expired
    Abstract:

    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).

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    Attachments: MindEarth
  • Study of Anion exchange membrane electrolyzers: improvements of the performance with the use of innovative functional materials
    This scholarship is granted by a collaboration with Enphos S.r.l.
    Deadline: August 25, 2021 Expired
    Abstract:

    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.

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    Attachments: Enphos s.r.l.
  • Development of a novel membrane based on anionic exchange for use in the electrolysis process
    This scholarship is granted by a collaboration with UFI Innovation Center S.r.l.​
    Deadline: August 25, 2021 Expired
    Abstract:

    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.

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  • Application of natural language processing technologies to clinical cases
    This scholarship is granted by a collaboration with Roche S.p.A.
    Deadline: August 25, 2021 Expired
    Abstract:

    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.

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  • University of Padua

  • Brain, Mind & Computer Science PhD program

  • Integrating logical reasoning and learning for recommendation systems
    Contacts: Luciano Serafini
    Deadline: May 12, 2021 Expired
    Abstract:

    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.

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    Attachments: Available positions
  • Personal agents for healthy coping interventions in healthcare
    Contacts: Silvia Gabrielli
    Deadline: May 12, 2021 Expired
    Abstract:

    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.

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    Attachments: Available positions
  • Generative Neural Models for profile-based Dialogues
    Contacts: Marco Guerini
    Deadline: May 12, 2021 Expired
    Abstract:

    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.

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    Attachments: Available positions
  • University of Bologna

  • PhD Programme in Electronics, Telecommunications, and Information Technologies Engineering

  • AI at the edge: end-to-end neural networks for audio processing on IoT devices
    Contacts: Alessio Brutti
    Deadline: May 21, 2021 Expired
    Abstract:

    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.

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  • Distributed embedded AI for energy-efficient smart sensing in IoT
    Deadline: May 21, 2021 Expired
    Abstract:

    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.

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  • University of Genoa

  • PhD Program in Security, Risk and Vulnerability

  • Programmable Network-wide Robustness and Security
    Deadline: June 15, 2021 Expired
    Abstract:

    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.

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  • Explainable Machine Learning in Network Security
    Deadline: June 15, 2021 Expired
    Abstract:

    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.

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  • Safety analysis for space and avionics systems and software
    Contacts: Marco Bozzano
    Deadline: June 15, 2021 Expired
    Abstract:

    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.

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  • Free University of Bozen

  • PhD in Computer Science

  • Ontology-mediated transformation of knowledge structures / Reasoning with weighted information
    Contacts: Loris Bozzato
    Deadline: June 30, 2021 Expired
    Abstract:

    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.

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  • Symbolic and sub-symbolic AI techniques for Process Mining
    Contacts: Chiara Ghidini
    Deadline: June 30, 2021 Expired
    Abstract:

    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);

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  • • Conversational AI for the medical domain

    • Explanatory dialogues for explainable AI

    Contacts: Bernardo Magnini
    Deadline: June 30, 2021 Expired
    Abstract:

    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.

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  • PhD in Advanced-Systems Engineering

  • Innovative printed nanomaterial for selective gas sensing applications
    Contacts: Andrea Gaiardo
    Deadline: June 30, 2021 Expired
    Abstract:

    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

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  • University of Udine

  • PhD Course in Computer Computer Science and Artificial Intelligence

  • • Integrating formal verification and testing for parametric software product lines

    • Reconfigurable and trustworthy pandemic simulation

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

    • Planning and scheduling with time and resource constraints for flexible manufacturing

    • Meta-learning for efficient 3D representations

    Interested applicants are invited to apply to the five themes. Up to four scholarships on four different themes will be awarded after a merit-based selection of the received applications
    Deadline: July 21, 2021 Expired
    Positions: 4
    Abstract:

    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.


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  • University of Ferrara

  • PhD Course in Physics

  • Artificial intelligence based solutions for data analysis in gas sensing application
    Contacts: Andrea Gaiardo
    Deadline: July 23, 2021 Expired
    Abstract:

    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.

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  • University of Rome - "La Sapienza"

  • Italian National PhD Program in Artificial Intelligence (PhD-AI.it) - Course on AI & security and cybersecurity

  • A formal approach to trustworthy autonomy
    Deadline: July 23, 2021 Expired
    Abstract:

    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.

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  • Automated Security Assistants for Confidential Computing
    Contacts: Silvio Ranise
    Deadline: July 23, 2021 Expired
    Abstract:

    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.

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  • TINY-ML for end-to-end audio processing on IOT devices
    Deadline: July 23, 2021 Expired
    Abstract:

    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.

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2020

  • University of Bologna

  • PhD Programme in Electronics, Telecommunications, and Information Technologies Engineering

  • AI on the edge: end to end neural networks for audio processing on IoT devices
    Deadline: May 21, 2020 Expired
    Abstract:

    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.

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    Attachments: Info and Call
  • AI-powered Infrastructure Security in Fog Computing Environments
    Deadline: May 21, 2020 Expired
    Abstract:

    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.

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    Attachments: Info and Call
  • Decentralised technologies for orchestrated fog computing intelligence
    Deadline: May 21, 2020 Expired
    Abstract:

    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.

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    Attachments: Info and Call
  • University of Genoa

  • PhD Program in Security, Risk and Vulnerability

  • Efficient, Secure and Privacy-Preserving Enforcement of Policies in Cloud-Edge Computing
    Contacts: Silvio Ranise
    Deadline: June 15, 2020 Expired
    Abstract:

    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.

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  • Integrating Security by Design and Automated Security Analysis for Digital Identity Management
    Contacts: Silvio Ranise
    Deadline: June 15, 2020 Expired
    Abstract:

    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

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  • Safety analysis for space and avionics systems and software
    Deadline: June 15, 2020 Expired
    Abstract:

    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.

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  • AI-based Network Security
    Deadline: June 15, 2020 Expired
    Abstract:

    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).

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  • University of Trento

  • Doctoral Program in Information and Communication Technology

  • Automatic analysis of radar/radar sounder images
    Contacts: Francesca Bovolo
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Data-driven techniques for closed-loop network and service management and RAN disaggregation in 6G Mobile Networks
    Contacts: Roberto Riggio
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Deep Learning Models for Human Behaviours
    Contacts: Bruno Lepri
    Deadline: June 16, 2020 Expired
    Abstract:

    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).

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  • Multi-/Hyper-temporal remote sensing image time series analysis
    Contacts: Francesca Bovolo
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Robust and multilingual hate speech detection
    Contacts: Sara Tonelli
    Deadline: June 16, 2020 Expired
    Abstract:

    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).

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  • Development of innovative Microsystems-based Radio Frequency RF-MEMS passives for next generation of telecommunications, wireless and radio systems
    Contacts: Iacopo Iannacci
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • SMT-based formal verification of parameterized systems
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Flexibility and Robustness in Speech Translation
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Doctoral School in Materials, Mechatronics and Systems Engineering

  • Energy-aware IoT Decentralized Smart Architectures
    Contacts: Fabio Antonelli
    Deadline: July 16, 2020 Expired
    Abstract:

    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.

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  • Doctoral Course in Cognitive Science

  • Computational thinking and educational technologies
    Deadline: July 23, 2020 Expired
    Abstract:

    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").

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  • Adaptive Personalized Game-based Motivational Systems
    Deadline: July 23, 2020 Expired
    Abstract:

    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).

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  • AI for new models of interactions (personal agents for mindfulness-based interventions and computational creativity)
    Deadline: July 23, 2020 Expired
    Abstract:

    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)

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  • Doctoral School in Mathematics

  • Computational modeling of infectious diseases
    Contacts: Piero Poletti
    Deadline: July 30, 2020 Expired
    Abstract:

    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.

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  • Doctorate Program in Industrial Innovation

  • Artificial Intelligence for Digital Transformation
    in collaboration with Dedagroup S.p.A.
    Deadline: August 25, 2020 Expired
    Abstract:

    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.

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    Attachments: Dedagroup S.p.A.
  • Formal Requirements Specification and Validation in Domain Specific Languages
    in collaboration with Intecs Solutions S.p.A.
    Deadline: August 25, 2020 Expired
    Abstract:

    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.

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  • Formal Methods for the Design of Safety Solutions in Automated Systems
    in collaboration with Bosch Research
    Deadline: August 25, 2020 Expired
    Abstract:

    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.

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    Attachments: Bosch Research
  • Application of Natural Language Processing Technologies to Clinical Cases
    Contacts: Bernardo Magnini
    Deadline: August 25, 2020 Expired
    Abstract:

    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).

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  • Doctoral Programme in Physics

  • Data Science for High Energy and Space Physics
    Deadline: August 28, 2020 Expired
    Abstract:

    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.

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  • Statistical physics of social dynamics and urban systems
    Deadline: August 28, 2020 Expired
    Abstract:

    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.

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  • Statistical physics and nonlinear dynamics of highly interdependent systems
    Deadline: August 28, 2020 Expired
    Abstract:

    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.

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  • University of Padua

  • Brain, Mind & Computer Science PhD program

  • Statistical relational learning on hybrid domains
    Contacts: Luciano Serafini
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Continual Computational Persuasion: A Framework for Adaptive Behavior Change Strategies
    Contacts: Claudio Giuliano
    Deadline: June 16, 2020 Expired
    Abstract:

    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).

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  • Immersive environments for the prevention and management of cognitive decay
    Contacts: Silvia Gabrielli
    Deadline: June 16, 2020 Expired
    Abstract:

    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.

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  • Free University of Bozen

  • PhD in Computer Science

  • Computational models of human behaviors
    Contacts: Bruno Lepri
    Deadline: July 13, 2020 Expired
    Abstract:

    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.

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  • Multi-perspective process mining
    Deadline: July 13, 2020 Expired
    Abstract:

    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.

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  • Data-driven Conversational Agents
    Contacts: Bernardo Magnini
    Deadline: July 13, 2020 Expired
    Abstract:

    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.

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  • University of Salento

  • Ph.D. Research Course in Engineering of Complex Systems

  • Ultra low power analog front-end architectures for Light Detection And Ranging (LiDAR)
    Contacts: Matteo Perenzoni
    Deadline: July 13, 2020 Expired
    Abstract:

    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.

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  • University of Udine

  • PhD course in Computer Science, Mathematics and Physics

  • Real-time Semantic Photogrammetry
    Contacts: Fabio Remondino
    Deadline: July 22, 2020 Expired
    Abstract:

    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.

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  • Self-Adaptive Automated Planning via Reinforcement Learning
    Deadline: July 22, 2020 Expired
    Abstract:

    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.

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  • Development of Silicon Photomultipliers for Next-generation Big Physics Experiments
    Contacts: Alberto Gola
    Deadline: July 22, 2020 Expired
    Abstract:

    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]

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  • Formal Methods for Automated Testing of Configurable Critical Software
    Deadline: July 22, 2020 Expired
  • Politecnico di Torino

  • Ph.D. in Electrical, Electronics and Communications Engineering

  • Development of Silicon Photomultipliers for Big Science and Industrial applications
    Contacts: Alberto Gola
    Deadline: September 11, 2020 Expired
    Abstract:

    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]

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2019