Calls 2023

Sony Semiconductor Solutions Group (a branch of Sony Corporation) is a corporate group that conducts research and development, product planning, design, production, and sales of semiconductor-related products, centering on image sensors in the imaging field such as smartphones and digital cameras, and in the sensing field such as automotive, security, and industry areas. A new R&D centre has been opened in Trento and is offering a PhD scholarship jointly with Fondazione Bruno Kessler. The objective of the collaboration within this scholarship is to study neural network architectures suitable for the integration within next generation advanced CMOS image sensors and optical sensors targeting to address the following challenges: i) fully parallel connection and processing between the top-level pixel array and the bottom layers digital processing blocks, ii) high reuse of on-chip memory and computational resources, iii) low power operation.
The doctoral candidates will have to tackle the challenges with a multidisciplinary point of view, starting from the design and simulations of ideal neural network architectures implementation, developing modelling of sensor+NN, investigating design trade-offs between computational power and chip integrability.
The outcome of the research would be the result of a feasibility study and a proposed set of features/ hardware architecture which would allow the start of a development in this field. The intellectual property of the research results that will derive from the activities carried out by the doctoral student is owned by FBK and Sony.

Research project aimed at developing generative models for omics data with particular attention
to single cell RNA seq, focusing on metrics engineering to test data likelihood, enriching the
latent space with biological information, exploring transfer learning for model generalization,
implementing algorithms for trajectory estimation and bifurcation analysis, enriching differential
gene expression analysis with probabilistic models, and developing robust approaches for
personalized medicine using optimization and reinforcement learning algorithms. The proposed
models will be applied to various datasets, including viral and bacterial infections and cancer, in
order to validate their ability to recapitulate experimentally observed cellular cascades.

Progetto di ricerca finalizzato allo sviluppo di modelli generativi per dati omici con particolare
attenzione al single cell RNA seq, lavorando sull'ingegnerizzazione di metriche per validare la
verosimiglianza dei dati, sull'arricchimento dello spazio latente con informazioni biologiche,
l'esplorazione del transfer learning per la generalizzazione del modello, l'implementazione di
algoritmi per l'analisi di traiettorie cellulari, l'arricchimento dell'analisi differenziale di espressione
genica con modelli probabilistici e lo sviluppo di approcci robusti per la medicina personalizzata
utilizzando algoritmi di ottimizzazione e reinforcement learning applicati ai modelli generativi. I
modelli proposti saranno applicati a vari set di dati, comprese infezioni virali, batteriche e
cancro, al fine di validare la loro capacità di ricapitolare le cascate cellulari osservate
sperimentalmente.

This PhD position calls for research into "foundation models," a type of deep learning neural network. These models, trained using vast amounts of data, serve as starting points for various tasks including, but not limited to, classification, regression, segmentation, and detection. However, the application of these models to the understanding of 3D scenes presents a challenge due to the unique characteristics of the training data. The proposed research aims to bridge this gap. The PhD candidate will primarily be tasked with exploring and constructing innovative vision algorithms rooted in deep learning and based on foundation models. The goal is to develop algorithms that can be effectively and flexibly utilised across diverse data types and domains.

The Internet of Things (IoT) is enabling and multiplying the point of collection of multimodal data such as audio, video and environmental data, typically processed in the cloud where potentially infinite computational power is available. However, this comes at the cost of bandwidth, energy and privacy. Recent research in the so-called tinyML domain is tackling the challenge of bringing artificial intelligence to the end-devices, thus limiting the need to stream data to the cloud and implementing the distributed intelligence paradigm in the cloud-edge continuum. Thus, novel approaches to enable AI, typically computationally demanding, on resource-limited devices are needed.  Exciting research scenarios are emerging to enable inference at the edge, ranging from distillation, quantization, or neural architecture search strategies to the fusion of software techniques with innovative hardware supporting tinyML. The complexity grows when we consider shifting not only inference but also learning to the edge to harness the opportunities offered by connected, distributed devices. The research proposed will focus on one or more of the following goals: (i) Develop novel hardware and software approaches to optimize AI on energy-efficient embedded devices, with a particular emphasis on audio processing and computer vision, while also considering other domains. (ii) Explore the potential of distributing and integrating intelligence from heterogeneous nodes in an IoT environment. (iii) Demonstrate the benefits of these approaches in real-world application scenarios, such as those encountered in smart cities.

This interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low-power Hardware will take into account the candidate's profile and interests, contributing to the development of innovative solutions for real-world challenges. The candidate will have the opportunity to work with cutting-edge technology, gain valuable experience in interdisciplinary collaboration, and make significant contributions to the field of tiny machine learning.

A better understanding of Earth climate and evolution relies on a better understanding or other planets. In 2023 European Space Agency (ESA) launched the JUpiter ICy moons Explorer (JUICE) mission to the Jovian system, and the development of the ESA EnVision mission to Venus is under development. Both missions carry on board a radar sounder instrument for subsurface sensing of planetary bodies. In the context of these two projects we are looking for candidates willing to design methodologies for sub-surface radar image processing and analysis. The outcome of this activity will contribute in improving the understanding of planetary subsurface structures, and their correlation to history and climate, as well as of Earth.

The candidate will be requested to design and develop novel methodologies based on artificial intelligence, deep learning and machine learning for information extraction, segmentation, classification, target detection, noise reduction and change detection in radar and radar sounder images.

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.

In recent years, there has been a significant increase in demand for secure and efficient systems to process data. In-network computing has emerged as a promising solution for offloading computation tasks, reducing latency, and relieving the workload of connected computing nodes. This technology uses smart network interface cards (smartNICs) to perform computation tasks on the network. However, limited adoption of this technology is due to the maturity of the software stack and related programming models, particularly for security applications.

This PhD scholarship aims to investigate many aspects of enabling in-network computing as a newer paradigm for solving security challenges, including cryptography. After conducting a literature review to identify relevant research and industry efforts in this area, focusing on existing systems such as NVIDIA Bluefield and programming models like DOCA or sPIN, the PhD candidate will identify challenges associated with adopting an in-network computing model for security and propose novel technical solutions. Additionally, the PhD candidate will conduct experimental evaluations to measure the performance and security of the proposed solutions (e.g., cryptographic algorithms implemented on smartNICs) and compare these results with traditional approaches. The findings of this research can guide future research and development in this area and can be applicable to industries that require secure and efficient data processing.

Applications are invited for a Ph.D. opportunity focused on secure data spaces for trustworthy data sharing in digital agriculture. The research aims to develop robust platforms for data sharing, privacy, and integrity in the agricultural landscape. Applicants should have a background in computer science or data management, with familiarity in agricultural systems and/or practices, data analytics, machine/deep learning, and algorithm design, validation, and benchmarking.

Techniques based on formal methods for the verification and validation of embedded and safety-critical software systems are becoming increasingly important, due to the growing complexity and importance of such systems in every aspect of modern society. Despite the major progress seen in the last twenty years, however, the application of formal methods in embedded software remains a challenge in practice, due to factors such as the interplay between computation and physical aspects and the increasing complexity of the software and its configurations.

This project will investigate novel techniques for the application of formal methods to the design, verification, and validation of embedded software, with particular emphasis on safety-critical application domains such as railways, automotive, avionics, and aerospace. The techniques considered will include a combination of automated and interactive theorem proving, satisfiability modulo theories, model checking, abstract interpretation, and deductive verification. Examples of the problems tackled during the project include the formal verification of functional requirements expressed in temporal logics, automated test-case generation, efficient handling of parametric/multi-configuration software systems and product lines, and the verification of software operating in a physical environment, subject to real-time constraints. Importantly, in addition to researching novel theoretical results, a significant part of the project activities will be devoted to the implementation of the techniques in state-of-the-art verification tools developed at FBK and their application to real-world problems in collaboration with our industrial partners.

In modern and heterogeneous learning environments, the one-size-fits-all approach is proven to be fundamentally flawed. Individualization through adaptivity is crucial to nurture individual potential, needs and motivational factors. The goal of this PhD thesis is to investigate the potential of combining gamification mechanics and adaptive personalized learning, analyzing the impact in terms of students’ achievements, participation and motivation. In particular, the PhD candidate will investigate AI-based theories and techniques for the development and validation of an  open, content-agnostic, and extensible platform for personalized playful learning. The platform will be validated in different formal and informal educational contexts. 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.

Social learning strategies are a key component of human intelligence and of our ability of learning from and collaborating with other humans in their environment. Inspired by this, some initial research efforts are enabling embodied AI agents with social and cooperative skills, thus permitting them to coordinate and collaborate with and to learn from other agents and humans. The goal of this PhD thesis is to devise innovative cooperative learning algorithms to support navigation of embodied AI agents in dynamic complex environments populated by other agents and humans, and to learn how to interact and to communicate with other heterogenous agents to learn collaboratively world models. The ideal candidate would have research interest in cooperative and embodied AI, multi-agent deep reinforcement learning, neuro-symbolic approaches, etc. The candidate will have the possibility of working within the ELLIS network and in collaboration with top international and national universities.

Planning and scheduling are techniques to automate and/or optimize decision-making. There is a breadth of applications that can benefit from the application of this kind of technique including (but not limited to) robotics, flexible manufacturing, logistics and people management. The aim of this PhD scholarship is to investigate and reinforce the applicability of this kind of technique considering the whole spectrum of domains that recently emerged from the AIPlan4EU (aiplan4eu-project.eu) project. The candidate will research innovative approaches and algorithms to improve the performance, usability and/or relevance of planning and scheduling techniques deployed in diverse scenarios, having the unique possibility to work and experiment with real-world scenarios of planning already deployed by the project.

Planning - devising a strategy to achieve a desired objective - is one of the basic forms of intelligence, with applications in autonomous robotics, logistics, flexible production, and many other fields. Historically, planning research has followed a general-purpose framework: a generic engine searches for the strategy by reasoning on the problem statement. Despite substantial progress in recent years, domain-independent planning still suffers from scalability issues and fails to deal with real-word problems. The alternative is to devise ad-hoc, domain-specific solutions that, although efficient, are costly to develop, rigid to maintain, and often inapplicable in non-nominal situations.

The PhD student will study the foundations of an innovative approach to Planning that will be domain-independent and efficient at the same time. The idea is to adopt a framework based on Reinforcement Learning, where a domain-independent planner is specialized with respect to the domain at hand. This research project will advance the state of the art in planning beyond the “efficiency vs flexibility” dilemma and provide effective techniques to be validated on real-world use-cases.

Hybrid systems are formal models combining discrete and continuous-time dynamic behaviors. They can be found in various applications such as robotics, control systems, cyber-physical systems, and transportation systems. Formal methods for hybrid systems provide a powerful set of techniques for designing, analyzing, and verifying the behavior of complex systems that exhibit both continuous and discrete behaviors. These techniques can be used to ensure the correctness and safety of the system and to detect design flaws and bugs early in the development cycle. This project will investigate new formal methods to prove properties of hybrid systems integrating model checking, automated theorem, and numerical analysis for control theory. Different aspects of hybrid systems will be considered including temporal properties, diagnosability and epistemic properties, reliability and robustness to faults. Compositional reasoning and proof synthesis will be also considered. The new methods will be implemented and evaluated on industrial benchmarks derived by industrial collaboration of FBK in various application domains such as space, avionics, autonomotive, railyways, and energy.

With an increasing availability and need of point clouds and 3D urban models, the inclusion of semantic information is becoming more and more important, in order to facilitate the usage and exploitation of such data. Traditional deep learning methods applied to 3D geospatial data suffer of generalisation, adaptation and explainability. Data annotation is also a major bottleneck, being time consuming and prone to errors.

The research topic should work with photogrammetric, RGB-D and LiDAR 3D data and:
(i) investigate self-supervised and unsupervised 3D classification methods, including few-shot or zero-shot learning
(ii) design models that can better adapt and generalise among scenarios
(iii) make 3D semantic segmentation results more explainable.

This research position calls for a highly motivated and skilled researcher who possesses a good combination of computer science, AI and geomatics knowledge.

This research aims to develop an innovative framework that combines optical sensors (e.g. cameras and LiDAR) and Internet of Things (IoT) technologies for real-time monitoring of civil infrastructures (e.g. bridges, buildings, dams, etc.). This research position calls for a highly motivated and skilled researcher who possesses a good combination of software expertise, hardware integration knowledge and fast prototyping capabilities. Therefore the successful candidate is supposed to have a strong ability to bridge software and hardware components, along with the agility to rapidly prototype innovative, reliable and replicable solutions. The interdisciplinary Phd is expected to provide a secure, replicable and reliable framework whose findings will be applicable to various industries, including transportation, construction, mining, etc., and could have significant implications for public safety and economic development.

Conversational agents are experiencing a surge in interest given the continuous release of new models and the ever evolving scenario of NLG. Still, the actual focus is mainly on model size, training data size and prompt engineering. The interaction of these elements with related aspects, such as decoding strategies, knowledge guided generation, data quality, knowledge distillation -just to mention a few- can help in improving the models, especially for better factuality, reducing hallucination and increasing coherence among dialogue turns. The goal of this PhD Thesis is to overcome the shortcomings of present large language models by incorporating novel strategies for better generation.

In spite of the recent progress in speech technologies, processing and understanding conversational spontaneous speech is still an open issue, in particular in presence of challenging acoustic conditions as those posed by dinner party scenarios. Although enormous progresses have been made recently in a variety of speech processing tasks (such as speech enhancement, speech separation, speech recognition, spoken language understanding), targeting also multi-speaker speech recognition, a unified established solution is still far from being available. Moreover, the computational complexity of current approaches is extremely high, making an actual deployment in low-end or IoT devices not feasible in practice.

The candidate will advance the current state-of-the-art in speech processing (in particular for separation, enhancement and recognition) towards developing a unified solution, possibly based on self-supervised or unsupervised approaches, for automatic speech recognition in dinner party scenarios, as those considered in the CHiME challenges (https://arxiv.org/abs/2306.13734).

Industrial systems are reaching an unprecedented degree of complexity. The process of designing a complex system is expensive, time consuming and error-prone. Moreover, the design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.

Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing a complex system. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW. Formal methods include technologies such as model checking, an automatic technique to symbolically and exhaustively analyze all possible executions of the system in the formal model, in order to detect design flaws as early as possible. Model checking techniques have been recently extended to assess the safety and dependability characteristics of the design, and for system certification.

The objective of this study is to advance the state-of-the-art in system design using formal methods. This includes adapting and extending the system design methodology, investigating improved versions of state-of-the-art routines for verification and safety assessment of complex systems, and developing novel extensions to address open problems. Examples of such extensions include novel techniques for contract-based design and contract-based safety assessment, advanced techniques for formal verification based on compositional reasoning, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, and the modeling of analysis of systems with continuous and hybrid dynamics.

This study will exploit the challenges and benchmarks defined in various industrial projects carried out at FBK.

Clinical neuroscience is playing a key role in the understanding of the brain with data of pathological alterations. The detection of anomalies in the brain structure and function is a crucial step not only for diagnosis and prognosis but also to decode the connectome of the human brain. Data driven approaches are providing promising results to characterize the patterns of the healthy brain. The challenge is to disentangle the intrinsic interindividual differences in the brain structure and function with respect to alterations related to cognitive impairment.

The research objective is to investigate the most innovative techniques of Artificial Intelligence, such as geometric deep learning, to translate the knowledge of connectivity structures from a healthy population to the individual patients of a clinical study. The ultimate goal is the development of computational methods to support the detection of altered structures in the connectome affected by brain disorders.

Conversational agents are experiencing a surge in interest given the continuous release of new models and the ever evolving scenario of NLG. Still, the actual focus is mainly on model size, training data size and prompt engineering. The interaction of these elements with related aspects, such as decoding strategies, knowledge guided generation, data quality, knowledge distillation -just to mention a few- can help in improving the models, especially for better factuality, reducing hallucination and increasing coherence among dialogue turns. The goal of this PhD Thesis is to overcome the shortcomings of present large language models by incorporating novel strategies for better generation.

This research aims to develop an innovative framework that combines 3D metrology, Internet of Things (IoT) and blockchain technologies for secure and trustworthy data management in real-time monitoring of civil infrastructures. The research will investigate the integration of IoT-enabled sensors and advanced 3D surveying techniques (SLAM, LiDAR, etc.) for continuous monitoring of structural health whereas blockchain technology will support secure and decentralized data storage and sharing.

The interdisciplinary Phd will contribute to advancing IoT, 3D metrology and blockchain technologies and their integration, providing a secure and reliable framework for real-time monitoring of civil infrastructure, such as bridges, buildings, dams or monument. The research findings will therefore be applicable to various industries, including transportation, construction, mining, etc. and could have significant implications for public safety and economic development.

Planning and scheduling are techniques to automate and/or optimize decision-making. There is a breadth of applications that can benefit from the application of this kind of technique including (but not limited to) robotics, flexible manufacturing, logistics and people management.

The aim of this PhD scholarship is to investigate and reinforce the applicability of this kind of technique considering the whole spectrum of domains that recently emerged from the AIPlan4EU (aiplan4eu-project.eu) project. The candidate will research innovative approaches and algorithms to improve the performance, usability and/or relevance of planning and scheduling techniques deployed in diverse scenarios, having the unique possibility to work and experiment with real-world scenarios of planning already deployed by the project.

Industrial systems are reaching an unprecedented degree of complexity. The process of designing a complex system is expensive, time consuming and error-prone. Moreover, the design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.

Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing a complex system. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW. Formal methods include technologies such as model checking, an automatic technique to symbolically and exhaustively analyze all possible executions of the system in the formal model, in order to detect design flaws as early as possible. Model checking techniques have been recently extended to assess the safety and dependability characteristics of the design, and for system certification.

The objective of this study is to advance the state-of-the-art in system design using formal methods. This includes adapting and extending the system design methodology, investigating improved versions of state-of-the-art routines for verification and safety assessment of complex systems, and developing novel extensions to address open problems. Examples of such extensions include novel techniques for contract-based design and contract-based safety assessment, advanced techniques for formal verification based on compositional reasoning, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, and the modeling of analysis of systems with continuous and hybrid dynamics.

This study will exploit the challenges and benchmarks defined in various industrial projects carried out at FBK.

Techniques based on formal methods for the verification and validation of embedded and safety-critical software systems are becoming increasingly important, due to the growing complexity and importance of such systems in every aspect of modern society. Despite the major progress seen in the last twenty years, however, the application of formal methods in embedded software remains a challenge in practice, due to factors such as the interplay between computation and physical aspects and the increasing complexity of the software and its configurations.

This project will investigate novel techniques for the application of formal methods to the design, verification, and validation of embedded software, with particular emphasis on safety-critical application domains such as railways, automotive, avionics, and aerospace. The techniques considered will include a combination of automated and interactive theorem proving, satisfiability modulo theories, model checking, abstract interpretation, and deductive verification. Examples of the problems tackled during the project include the formal verification of functional requirements expressed in temporal logics, automated test-case generation, efficient handling of parametric/multi-configuration software systems and product lines, and the verification of software operating in a physical environment, subject to real-time constraints. Importantly, in addition to researching novel theoretical results, a significant part of the project activities will be devoted to the implementation of the techniques in state-of-the-art verification tools developed at FBK and their application to real-world problems in collaboration with our industrial partners.

Planning – devising a strategy to achieve a desired objective – is one of the basic forms of intelligence, with applications in autonomous robotics, logistics, flexible production, and many other fields. Historically, planning research has followed a general-purpose framework: a generic engine searches for the strategy by reasoning on the problem statement. Despite substantial progress in recent years, domain-independent planning still suffers from scalability issues and fails to deal with real-word problems. The alternative is to devise ad-hoc, domain-specific solutions that, although efficient, are costly to develop, rigid to maintain, and often inapplicable in non-nominal situations.

The PhD student will study the foundations of an innovative approach to Planning that will be domain-independent and efficient at the same time. The idea is to adopt a framework based on Reinforcement Learning, where a domain-independent planner is specialized with respect to the domain at hand. This research project will advance the state of the art in planning beyond the “efficiency vs flexibility” dilemma and provide effective techniques to be validated on real-world use-cases.

In modern and heterogeneous learning environments, the one-size-fits-all approach is proven to be fundamentally flawed. Individualization through adaptivity is crucial to nurture individual potential, needs and motivational factors.

The goal of this PhD thesis is to investigate the potential of combining gamification mechanics and adaptive personalized learning, analyzing the impact in terms of students’ achievements, participation and motivation. In particular, the PhD candidate will investigate AI-based theories and techniques for the development and validation of an  open, content-agnostic, and extensible platform for personalized playful learning. The platform will be validated in different formal and informal educational contexts.

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.

This research aims to investigate the design of secure and interoperable data spaces and data governance frameworks for the management and sharing of agricultural data in the context of Agriculture 4.0. The study will explore the technical and organizational challenges of establishing and governing data spaces, which are virtual environments for managing, sharing, and analyzing data. The research will examine the potential benefits of data spaces in agriculture, such as improving crop yields, reducing environmental impact, and enhancing the overall efficiency of agricultural operations. The study will explore how data governance frameworks can be designed to ensure data privacy, security, and accountability in the agriculture industry. The research will investigate the technical and economic factors that influence the adoption and implementation of these frameworks, including issues related to data quality, interoperability, and standardization. The study will also explore how emerging technologies, such as blockchain, edge computing, and machine learning, can enhance the security and governance of data spaces in agriculture. The research will identify strategies for optimizing these technologies and frameworks to promote innovation, collaboration, and sustainability in Agriculture 4.0.

This research aims to investigate the integration of robotics and IoT technologies with deep learning-based IoT Data analysis techniques in the context of digital agriculture. The study will focus on addressing the challenges and opportunities that arise from the combination of these technologies, with a particular emphasis on the use of deep learning algorithms for the mass collection and analysis of agricultural data provided by IoT-distributed sensors. The research will explore how deep learning can improve the efficiency, sustainability, and productivity of agricultural practices by enabling automatic and semi-automatic training of algorithms for classification/analysis. The study will also examine the technical and economic factors that may influence the adoption and implementation of these technologies in the agricultural sector to identify strategies to optimize their use and impact.

Hybrid systems are formal models combining  discrete and continuous-time dynamic behaviors. They can be found in various applications such as robotics, control systems, cyber-physical systems, and transportation systems. Formal methods for hybrid systems provide a powerful set of techniques for designing, analyzing, and verifying the behavior of complex systems that exhibit both continuous and discrete behaviors. These techniques can be used to ensure the correctness and safety of the system and to detect design flaws and bugs early in the development cycle.

This project will investigate new formal methods to prove properties of hybrid systems integrating model checking, automated theorem, and numerical analysis for control theory. Different aspects of hybrid systems will be considered including temporal properties, diagnosability and epistemic properties, reliability and robustness to faults. Compositional reasoning and proof synthesis will be also considered. The new methods will be implemented and evaluated on industrial benchmarks derived by industrial collaboration of FBK in various application domains such as space, avionics, autonomotive, railyways, and energy.

Social learning and learning of cause-and-effect relationships are key components of human intelligence. The goal of the current PhD thesis is to model and evaluate notions of social learning, social influence, and counterfactual and causal learning in order to improve the performance of a group of AI agents and their ability to produce explainable decisions in scenarios where they have to interact with humans. Additionally, the thesis may also investigate the properties of empirical social networks (for example, sparsity) to organize the topology of communications, interactions, and cooperation that occur between a multiplicity of AI agents.

The ideal candidate will have research interests on multi-agent deep reinforcement learning, complex networks, causal learning. The candidate will have the possibility of working within the ELLIS network and in collaboration with top international universities and research centers. Contacts: [email protected]; [email protected]

SPAD arrays in CMOS technology are single photon detectors providing < 100 ps time resolution. They are used in a wide spectrum of time-resolved imaging systems, such as medical and biomedical imaging (e.g., fluorescence lifetime imaging microscopy, Raman spectroscopy, and diffuse optical tomography), depth sensing for industrial, automotive, space and consumer applications, and quantum imaging (super-resolution microscopy, ghost imaging). These systems rely on the high temporal resolution of SPAD detectors combined with time-to-digital converters to generate precise timestamps of individual photons, which are then processed to extract the required information (lifetime, time-of-flight, etc). In case of large arrays of SPADs, the readout channel becomes the bottleneck of the system, demanding the integration of on chip of power- and area-efficient custom digital signal processors. The objective of this project is to develop hardware friendly algorithms for the specific challenges identified above, test them using existing sensors and then designing custom processors to be integrated on-chip. The student will interact with experts in the fields of single-photon image sensors, and analog/mixed signal integrated circuit design, gaining a unique combination of background knowledge. The expected outcome is the realization of state-of-the-art image sensors and their validation in a real use-case scenario.

The widespread use of social media has transformed the way people interact and communicate with each other. The interactions that occur on social media create a network that facilitates the transmission of information across large groups of people. This network can be used to understand how information flows through society and how it shapes people's beliefs and opinions.

One way to understand the social network that is created through online interactions is to analyze the textual information contained within user profiles and messages. This information can be used to characterize users, identify emerging topics, and extract information about cultural characteristics. By studying the patterns of interactions between users, it is possible to identify their stance on specific topics, communication dynamics and communities sharing the same opinions.

However, understanding the social network alone is not enough to gain a complete understanding of how information is transmitted and how it shapes society. It is also important to consider the physical location of these groups and how it affects their interactions and the diffusion of information across the social network. This is because physical space can play a significant role in shaping social dynamics and the diffusion of information. For example, social movements may arise in response to local events, and these movements may have a different impact in different geographic locations.

The interplay between the structure of the social network and the semantic information that diffuses across it is a complex phenomenon that requires a multidisciplinary approach, and that can be further enriched with geographical information. This PhD thesis will employ various research methods, including data mining, network analysis, Natural Language Processing (NLP), and geographic information systems (GIS), to explore this interplay. By examining the relationship between the social network and semantic information, the research aims to provide insights into how social media shapes society and how it can be used to better understand the dynamics of cultural groups, minorities, and social movements of opinion. Ultimately, the findings of this research may have important implications for fields such as sociology, anthropology, and communication studies, as well as for policymakers and social media companies.

In recent years, there has been a significant increase in demand for secure and efficient systems to process data. In-network computing has emerged as a promising solution for offloading computation tasks, reducing latency, and relieving the workload of connected computing nodes. This technology uses smart network interface cards (smartNICs) to perform computation tasks on the network. However, limited adoption of this technology is due to the maturity of the software stack and related programming models, particularly for security applications.

This PhD scholarship aims to investigate many aspects of enabling in-network computing as a newer paradigm for solving security challenges, including cryptography. After conducting a literature review to identify relevant research and industry efforts in this area, focusing on existing systems such as NVIDIA Bluefield and programming models like DOCA or sPIN, the PhD candidate will identify challenges associated with adopting an in-network computing model for security and propose novel technical solutions. Additionally, the PhD candidate will conduct experimental evaluations to measure the performance and security of the proposed solutions (e.g., cryptographic algorithms implemented on smartNICs) and compare these results with traditional approaches. The findings of this research can guide future research and development in this area and can be applicable to industries that require secure and efficient data processing.

The advent of foundation models has introduced unprecedented opportunities in all areas of natural language processing. Automatic translation (be it speech or text translation) is no exception, with a wide variety of language directions, domains and application scenarios whose coverage is no longer a mere utopia. Although conditions today are more favorable than in the past, open challenges still exist in terms of fully exploiting the power of the available models,  increasing their flexibility to integrate diverse input types, or constraining the output to meet specific application requirements. Open questions include: how to feed non-symbolic models with symbolic information describing the context of a translation request? How to supply meta-information about target users? How to integrate model capabilities with external information from structured knowledge bases? How to condition the output to specific target applications? This PhD aims to explore state-of-the-art solutions to tackle these challenges, with a special focus on the integration of multimodal information (e.g. contextual information supplied as visual cues), user-specific constraints (e.g. for gender/formality control), and application-specific constraints (e.g. structural requirements as in the case of video subtitling).

Machine learning and deep neural networks have proven to be highly effective in processing multimodal data, such as audio, video, and environmental data, on powerful computing systems. However, a major challenge in artificial intelligence is how to extend these capabilities to resource-constrained devices, such as end nodes, in an IoT system. Fortunately, recent advancements in TinyML approaches are opening up new opportunities to bring AI to the far edge of the edge-to-cloud continuum. Exciting research scenarios are emerging that span from tiny deep learning solutions for inference on resource-constrained platforms, which rely on distillation, quantization, or neural architecture search, up to combining software techniques with innovative hardware that support TinyML. Moreover, the complexity grows when we consider moving learning to the edge to take advantage of the opportunities presented by connected, distributed devices. To address these challenges, this research aims to (i) develop novel hardware and software approaches for optimizing AI on energy-efficient embedded devices, with a particular focus on audio processing and computer vision, but not limited to these areas; (ii) explore the potential of distributing and fusing intelligence from heterogeneous nodes in an IoT; and (iii) demonstrate the benefits of these approaches in real-world application scenarios, such as those found in smart cities. The candidate's profile and interests will be considered when structuring this interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low power hardware, contributing to the development of innovative solutions for real-world challenges. The candidate will have the opportunity to work on cutting-edge technology, gain experience in interdisciplinary collaboration, and make significant contributions to the field of tiny machine learning.

In spite of the recent progress in speech technologies, processing and understanding conversational spontaneous speech is still an open issue, in particular in presence of challenging acoustic conditions as those posed by dinner party scenarios. Although enormous progresses have been made recently in a variety of speech processing tasks (such as speech enhancement, speech separation, speech recognition, spoken language understanding), a unified established solution is still far from being available. In particular, one of the limitations of the current approaches is their computational complexity that makes an actual deployment in low-end or IoT devices not feasible in practice.

The candidate will advance the current state-of-the-art in speech processing (in particular for separation and enhancement) towards developing a unified solution, possibly based on self-supervised or unsupervised approaches, for automatic speech recongition in dinner party scenarions (as those considered in the CHiME challenges).

In 2023 there will be the launch of the European Space Agency (ESA) mission JUpiter ICy moons Explorer (JUICE) to the Jovian system, and the development of the ESA EnVision mission to Venus will continue. Both missions carry on board a radar sounder instrument for subsurface sensing of planetary bodies. In the context of these two projects we are looking for candidates willing to design methodologies for sub-surface radar image processing and analysis. The outcome of this activity will contribute in improving the understanding of subsurface structures, and their correlation to planetary body history and climate.

The candidate will be requested to design and develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection, noise reduction and change detection in radar and radar sounder images.

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.

Nowadays a huge amount of remote sensing data is available being acquired with a high temporal, spatial and spectral resolution. Those data are coming from several missions, among the others: ESA Copernicus (Sentinels), ASI PRISMA and COSMO-SkyMed, and future IRID constellation. The management and use of such data requires the design of novel solutions being able to handle long time series of dense but irregular data worldwide in the context of high-power computing systems looking both back and forth in time.

Candidates will be requested 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 time series of remote sensing images.

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.

Nowadays, microelectromechanical systems (MEMS) represent a well-established class of miniaturized devices integrating both mechanical and electrical components onto the same silicon board, fabricated together via photolithography-based process, which enables small device footprint, high sensing/actuating performances, low-power consumption, and mass production. MEMS devices, with particular reference to inertial sensors, are commonly applied in a variety of fields, ranging from consumer electronics to automotive industry, and have enabled novel functions and opportunities of the systems where they have been applied (e.g., screen rotation in mobile phones). However, they are still scarcely used in high-demanding applications, such as aerospace, because of insufficient sensitivity and stability, stemming from technological limitations affecting current fabrication strategies.

In this research project, novel technological solutions and mechanisms will be sought to develop MEMS inertial sensors based on Silicon-On-Insulator (SOI) wafer micromachining to enable high sensitivity and low noise, compared to standard devices. Different layouts will be developed and their mechanical and electro-mechanical performance compared through both analytical and numerical models, based on Finite Elements Method (FEM). The optimized layouts will be then fabricated and experimentally validated.

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

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

This Ph.D. works within this context with the aim of exploiting behavior change intervention (BCI) strategies to design novel persuasive frameworks nurturing AI-based systems to trigger the implementation of the next-generation virtual digital assistants.

The area of intervention is very broad since the research areas involved are, for example, human-computer interaction, psychology, computation linguistics, knowledge management, and pervasive computing.

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

The Ph.D. candidate will also have the opportunity to validate her/his solutions in several real-world settings with the aim of validating her/his work in practice.

The aim of this Ph.D project is to explore innovative, technology-based approaches to facilitate playful education for inclusive, collaborative and student-centered learning environments. The project aims to develop semi-automatic and adaptive educational paths, as well as personalized support systems that benefit both educators and learners. This will involve utilizing various technologies and methods, such as end-user programming or multimodal, competence-based learning, to co-create learning activities that consider specific needs and preferences.
The ideal candidate will have a background in Computer Science, Psychology or Cognitive Science, with knowledge of data science, artificial intelligence, and educational theories. Experience in designing interactive digital technologies, conducting experimental and in-the-wild studies, and promoting inclusive, collaborative learning environments would be advantageous and should be gained during the Ph.D training.

This project will develop an innovative platform in thin film lithium niobate for performing quantum optics experiments with integrated devices. It will focus on the development of innovative sources for single photons and squeezed vacuum based on periodically poled waveguides and reconfigurable circuits, and on the integration of superconducting single photon detectors on-chip. This project is part of an on-going collaboration between the Department of Industrial Engineering at the University of Trento, the Bruno Kessler Foundation (FBK), the Italian Institute for Nuclear Physics (INFN), and the National Research Council (CNR).

The fundamental objective of the PhD activity is the development of prototype cells of ionic conductive cells. It is possible to divide the planned activities into two main activities, closely related to each other and aimed at optimizing the devices and their performance. First activity will be performed in strong collaboration with UniTN-DII, concerning the production of materials and the realization of the prototype cells. Reference will be made to planar architectures where, initially, the compositions will refer to an electrolyte based on zirconia or Ceria suitably doped, hydrogen side electrode based on zirconia/nickel/copper or perovskites such as ferrites, manganites or cobaltites, the latter to be also use for the oxygen side electrode; compositions and structures will be identified above all on the basis of the expected operating temperatures and suitably modified in order to optimize performance, always keeping in mind the production process which will have to allow for the achievement of multilayer planar structures "alla ceramic". The prototype cells will be made using colloidal technologies (tape casting, screen printing, digital printing) and subsequent co-sintering to then be characterized from a chemical-physical and structural point of view to be able to correlate the performances as identified in the subsequent activity. The second activity provides for the physicochemical, structural, and electrochemical characterization in electrolysis and fuel cell mode of the cells, in collaboration with FBK.

The candidate will work in the Integrated and Quantum Optics group, FBK, where he will develop technologies for integrated photonics.

The technological platforms on which the candidate will operate will be mainly Lithium Niobate On Insulator (LNOI) and silicon. LNOI is one of the most interesting materials for integrated photonics due to the wide transparency window and its non-linear optical properties. The strong and fast electro-optic responsivity of Lithium Niobate will be exploited to design fast modulators and highly efficient correlated photon sources.

The candidate will develop techniques for thin films of LNOI processing aimed at the realization of such photonic devices on the platform. The candidate will exploit the technological platform to design optical devices and components, that he/she will fabricate and characterize in the laboratories of FBK and UniTN.

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 makes RFBs ideal for economical, large-scale energy storage. However, cost reductions are needed for 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 deployment of RFBs. RFBs are a complex system so a trade-off between cost and performances must be found for both electrolytes and stack.

This work will focus on developing and validating the use of low-cost earth-abundant redox active materials (e.g., Al, Zn, Fe, Cu, Mn, I, S) in redox flow batteries analyzing different possible solutions for redox couples and electrolytes. The doctoral thesis will start with the development of an integrated approach for the evaluation of different electrolyte solutions (combining different redox active materials, supporting electrolytes and solvents) assessing material cost and availability (with a special focus on EU-sourced materials), safety, expected performances, probability of upscaling issues (e.g., cross-over, pumping losses, etc) and other parameters in other to select the most promising solutions. This pre-selection will be refined by integrating experimental data obtained from the electrochemical and physical characterization of the electrolytes. The use of solid boosters and/or complexing agents to improve the energy density will be a fundamental step to be evaluated experimentally. The selected best candidates will be then tested in lab-scale redox flow cells customized for the specific chemistry. Membrane/separator selection or development will also be addressed during this phase. Finally, issues related to the upscaling of the technology will also be analyzed in order to assess the real potential impact of the proposed innovative solutions.

This research aims to investigate the integration of distributed learning paradigms with robotics, IoT, and edge computing in the context of digital agriculture. It will explore the potential benefits of using distributed learning approaches, such as federated learning, continual learning, and reinforcement learning to improve crop yields and reduce costs and environmental impact by enhancing the overall efficiency of agricultural operations, also including active sensing, autonomous navigation, and decision-making. The research will explore IoT and edge computing to support the collection, processing, and analysis of data from distributed sensors in the field. The study will investigate the technical and economic factors that influence the adoption and implementation of these technologies and frameworks in agriculture, including issues related to scalability, resource constraints, and interoperability. Finally, the research will also explore the potential of combining these technologies and frameworks to create new opportunities for innovation and collaboration in digital agriculture.

Space systems have reached an unprecedented degree of complexity. The design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.

Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing complex systems, in different domains. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW.

The objective of this study is to advance the state-of-the-art in space system design using formal methods. In particular, it will investigate new techniques for model-based system and software engineering, to support the design, mission preparation and operations of space systems. The potential research directions include fault detection, isolation, and recovery for satellites; system level diagnosis and diagnosability based on telemetry; digital twins for satellites. Topics to be investigated include techniques for contract-based design and contract-based safety assessment, advanced verification techniques based on compositional reasoning, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, and the modeling and analysis of systems with continuous and hybrid dynamics.

The developed techniques will be implemented and evaluated using tools for system-software engineering such as the COMPASS tool and the COMPASTA tool, based on the TASTE tool chain.

Water phase changes (evaporation, condensation, freezing) are ubiquitous phenomena of great importance for living beings and in engineering applications. The structure (micro and nano) and chemistry of surfaces control the kinetics and dynamics of these transitions. Plants, for example, offer numerous examples of self-cleaning, antifreeze and water-collecting1 properties developed over millions of years of evolution. Engineered anti-frosting surfaces find applications in aerospace (ice accretion on aircrafts), heat exchangers (refrigerators), wind turbines and power lines. Structured surfaces that increase evaporation and condensation efficiency are a challenge for Loop Heat Pipes (LHP) and Vapour Chambers that cool electronics on space stations (in microgravity conditions) or in the electronic devices we use on a daily basis. Surfaces that can efficiently collect dew and fog provide a source of water in arid environments and can improve the water recovery system of space stations.

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

The European Space Agency (ESA) mission JUpiter ICy moons Explorer (JUICE) to the Jovian system has  been launched in April 2023, and the development of the ESA EnVision mission to Venus will continue. Both missions carry on board a radar sounder instrument for subsurface sensing of planetary bodies. In the context of these two projects we are looking for candidates willing to design methodologies for sub-surface radar image processing and analysis. The outcome of this activity will contribute in improving the understanding of subsurface structures, and their correlation to planetary body history and climate.

The candidate will be requested to design and develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection, noise reduction and change detection in radar and radar sounder images.

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.

This grant is funded by project ASI JUICE-RIME-E - “Missione JUICE - Attività dei team scientifici dei payload per lancio, commissioning, operazioni e analisi dati” — CUP F83C23000070005.

As we gather increasing volumes of data from next-generation sequencing and multi-omics technologies, the potential to develop transfer learning and neural networks applicable to diverse fields expands. A central challenge that remains is the creation of embeddings capable of representing information in a biologically meaningful space. The engineering and characterization of a model capable of representing this information in a comprehensive and robust manner could revolutionize the field with an impact comparable to what word2vec did for natural language processing.

The prospective PhD candidate is expected to work on the development of a model capable of recapitulating the entire biology of selected organisms, characterizing the manifold in terms of gene ontologies and pathway enrichments. The resulting encoding capabilities should be usable by the scientific community at large, and result in significant advancements in the field such as images to RNA inference models and vice versa.

Through this endeavor, we aim to make a significant stride in unifying diverse data types under a shared, biologically meaningful representational framework. This study will not only advance our understanding of biological systems but also serve as a herald for a new wave of AI applications in bioinformatics.

With the aging of global population, the prevalence of neurodegenerative diseases (among
which Alzheimer Disease, Parkinson’s Disease and Multiple Sclerosis) is rapidly increasing, representing a challenge for the sustainability of the healthcare systems in the forthcoming decades. Machine learning represents a promising tool to build models for the prediction of disease course and the targeting of therapeutic and care strategies. Nevertheless, traditional AI tools require lots of curated data to provide robust and reliable predictions, which might be a challenge when dealing with longitudinal clinical data. Indeed, clinical data are challenging to obtain due to privacy concerns and, whenever available, they might present many missing values, rendering them unsuitable for the training of AI algorithms.
The objectives of this research are two fold. The first is to build predictive models of the
development of several neurodegenerative diseases to assist clinicians in decision making.
The second objective is to investigate methods to mitigate data scarcity challenges. These
methods will exploit similarities between these diseases and will include a range of approaches such as transfer learning and generative models. Each of these approaches will
then be evaluated in terms of predictive performance in a set of clinically relevant outcomes.

Clinical neuroscience is playing a key role in the understanding of the brain with data of pathological alterations. The detection of anomalies in the brain structure and function is a crucial step not only for diagnosis and prognosis but also to decode the connectome of the human brain. Data driven approaches are providing promising results to characterize the patterns of the healthy brain. The challenge is to disentangle the intrinsic interindividual differences in the brain structure and function with respect to alterations related to cognitive impairment.

The research objective is to investigate the most innovative techniques of Artificial Intelligence, such as geometric deep learning, to translate the knowledge of connectivity structures from a healthy population to the individual patients of a clinical study. The ultimate goal is the development of computational methods to support the detection of altered structures in the connectome affected by brain disorders.

The objective of this thesis is to advance the mathematical theory of Bayesian Neural Networks for both Shallow and Deep Networks. These kind of networks are very promising since their flexibility in modeling the phenomenon under study as well as to provide accuracy measures of the estimated quantities a problem not well understood in classic network theory. Computational aspects would another aspect cover by this thesis since standard methods for network training will not be suitable for real applications. The other objective of this thesis is the applications of the new methodology to important problems in Health Sciences for e.g. Omics or  Bioimaging data. The study will involve expertise in several topics including Bayesian non parametrics methods, information geometry, Monte Carlo methods as well as standard methods in Deep Learning. The developed methods will be tested on different life sciences datasets (separatly and integrated) such as EHR, Omics and bioimaging (e.g. CT, PET, digital pathology), both publicly available and originally produced by DSH partner labs, on different tasks.

The objective of this thesis is to advance mathematical models and analytical tools for the investigation of spatially embedded networks and their influence on urban systems. The study will utilize the expertise of several fields, including graph theory, topology, geometry, and spatial statistics, to analyze the structure, function, and dynamics of spatial networks.

Through our research, we aim to contribute to the comprehension of urban systems by developing appropriate mathematical and statistical tools with the goal of providing insightful information to policy decisions concerning urban planning and transportation. Our study will focus on (i) theoretical properties of spatially embedded networks (ii) their design for modeling urban systems and (iii) the statistical tools for their analysis.

To achieve these objectives, we will adopt a multidisciplinary approach that will advance our understanding of spatially embedded networks as a mathematical model and their use in the analysis of urban systems. Our focus will be on the development of mathematical models that effectively capture the complexity of urban spatial networks such as transportation and utility networks. The analysis will try to identify patterns, predict outcomes, and classifying spatial networks based on their properties.

Computational issues are an aspect that will be covered by the project as well. By combining traditional statistical methods with machine learning algorithms, we will be able to process large networks to extract insights that would be otherwise challenging to obtain. Moreover, machine learning techniques will be utilized to optimize network design and allocate resources, resulting in more sustainable and efficient urban systems.

Candidates will learn and apply mathematical modeling techniques for the study of infectious disease dynamics and control, with a special focus to their deployment within important public health issues. Compartmental modeling, individual-based modeling and Bayesian approaches will be among the main tools adopted to this aim. Infections due to respiratory or mosquito-borne pathogens and antimicrobial resistant microorganisms will be among the main possible topics of the research. Potential applications are the quantitative estimation of critical parameters and their population heterogeneity, the understanding of mechanisms of transmission and disease, the assessment of effectiveness of public health interventions and the development of epidemiological scenarios, including those relevant for pandemic preparedness and response. The PhD will take place at the Center for Health Emergencies (CHE) of the Fondazione Bruno Kessler under activities of the PNRR-funded INF-ACT project, in which the CHE leads the development of mathematical modeling activities within a partnership involving most of the major Italian research and public health institutions in the field of infectious diseases.

The PhD candidate will focus on the development and microfabrication of superconducting quantum devices, such as devices based on Josephson junctions and/or devices based on superconducting high-kinetic-inductance films. The candidate will be involved in the design and simulations and in the microfabrication of the devices, exploiting the cleanroom facilities at Fondazione Bruno Kessler. The cleanroom processes for the microfabrication of the single circuitry elements, such as Josephson junctions, microwave resonators and high-kinetic-inductance films, will be optimized. Subsequently, the optimized processes will be exploited for the fabrication of the final quantum devices, which will be characterized at millikelvin temperature by means of dilution refrigerators. The results of the measurements and of the corresponding data analysis will serve to further optimize the device in order to improve the performance. The optimized devices (Josephson junctions-based devices and/or Kinetic Inductance Travelling Wave Parametric Amplifiers) will be exploited to investigate fundamental physics processes, such as microwave squeezing and generation of entangled microwave photons. The produced devices will be finally combined to create a complete read-out chain for multiqubit systems.

CMOS Application Specific Integrated Circuits (ASIC) coupled with silicon radiation detectors provide the best performance in terms of noise, power consumption and compactness in a wide range of applications. Some of them include: biomedical spectroscopy or tomography, Earth-based or space telescope for monitoring x or gamma ray events coming from deep space, physic experiments, security scanning, and cultural heritage monitoring.
The system detects the photons that are converted in a voltage/current signal processable by dedicated mixed-signal electronics implementing different on-chip functionalities, such as photon counting, energy spectrum, energy-based discrimination and imaging. In this field, today's main challenges are the achievement of low electronic noise and power consumption, combined with high photon rates working operations.
The goal of this project is to develop a CMOS ASIC specifically designed for silicon radiation detector as, for example, Silicon Drift Detector (SDD), Low Gain Avalanche Diode (LGAD) and Silicon Photon Multiplier (SiPM), focusing on low-noise, high-rate and low power consumption trade-off.
The student will interact with experts in the fields of radiation sensors and analog/mixed signal integrated circuit design, gaining a unique combination of background knowledge. The expected outcome is the realization of state-of-the-art ASIC for radiation detectors and their validation in a real use-case scenario.

Nowadays, digital identities are employed by the majority of European governments and private enterprises to provide a wide range of services, from secure access to social networks to online banking. As the Digital 2023 global overview report shows, the number of digital identities is growing: we have 4.76 billion social media users and spend trillions of dollars on e-commerce.
Digital identity is therefore a key ingredient for securing new IT systems and digital infrastructures such as those based on zero trust. For these reasons, the secure deployment of digital identity solutions is a mandatory prerequisite for building trust in digital ecosystems and is an obligation shared by security practitioners and consumers.
The research work to be conducted in the thesis aims to develop a new approach for automated security, privacy, and risk management in the design, development, and maintenance of digital identity solutions. The challenge is to deal with the multiple dimensions of the design space as a continuum in which specifications are analyzed both in isolation and as refinements of each other.
The approach should take into account the specific security and privacy issues of each phase and, at the same time, consider the interdependencies among the design and implementation choices performed in the various phases, bridging the gap among them.
The resulting approach should be automated, auditable, provide actionable hints to reduce risk, and be easy to integrate into the wide range of services and applications that arise in the plethora of use case scenarios resulting from the pressure of digital transformation. This activity includes:
- Analysis of state-of-the-art identity management solutions and their security issues.
- Identification of relevant use cases.
- Specification of a (semi-)automatic approach for security and risk management of digital identity solutions.
- Implementation of the approach on a tool and experimental evaluation on real-world use cases.

References:
M. Pernpruner, R. Carbone, G. Sciarretta and S. Ranise. An Automated Multi-Layered Methodology to Assist the Secure and Risk-Aware Design of Multi-Factor Authentication Protocols. Submitted to IEEE Transactions on Dependable and Secure Computing (TDSC).
G. Sciarretta, R. Carbone, S. Ranise, L. Viganò. Formal analysis of mobile multi-factor authentication with single sign-on login. ACM Transactions on Privacy and Security (TOPS) 23 (3), 1-37.
M. Pernpruner, R. Carbone, S. Ranise, G. Sciarretta. The Good, the Bad and the (Not So) Ugly of Out-of-Band Authentication with eID Cards and Push Notifications: Design, Formal and Risk Analysis. In Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy (pp.223-234), 2020.
A. Sharif, M. Ranzi, R. Carbone, G. Sciarretta, F. A. Marino, S. Ranise. The eIDAS Regulation: a Survey of Technological Trends for European Electronic Identity Schemes. MDPI Journal of Applied Science (APPLSCI), 2022.

Industrial systems are reaching an unprecedented degree of complexity. The process of designing a complex system is expensive, time consuming and error-prone. Moreover, the design process has to guarantee not only the functional correctness of the implemented system, but also its dependability and resilience with respect to run-time faults. Hence, the design process must characterize the likelihood of faults, mitigate possible failures, and assess the effectiveness of the adopted mitigation measures.

Formal methods have been increasingly used over the last decades to deal with the shortcomings of designing a complex system. Formal methods are based on the adoption of a formal, mathematical model of the system, shared between all actors involved in the system design, and on a tool-supported methodology to aid all the steps of the design, from the definition of the architecture down to the final implementation in HW and SW. Formal methods include technologies such as model checking, an automatic technique to symbolically and exhaustively analyze all possible executions of the system in the formal model, in order to detect design flaws as early as possible. Model checking techniques have been recently extended to assess the safety and dependability characteristics of the design, and for system certification.

The objective of this study is to advance the state-of-the-art in system design using formal methods. This includes adapting and extending the system design methodology, investigating improved versions of state-of-the-art routines for verification and safety assessment of complex systems, and developing novel extensions to address open problems. Examples of such extensions include novel techniques for contract-based design and contract-based safety assessment, advanced techniques for formal verification based on compositional reasoning, the analysis of the timing aspects of fault propagation, the characterization of transient and sporadic faults, the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns, and the modeling of analysis of systems with continuous and hybrid dynamics.

This study will exploit the challenges and benchmarks defined in various industrial projects carried out at FBK.

Applications that rely on the most modern sensing devices and technologies and combine complex artificial intelligence tasks are now mainstream. It is sufficient to say, “OK-Google/Alexa/Siri switch on the heating system when the temperature is below 18° C” to appreciate the power of the IoT in combination with an Artificial Intelligence engine. However, the typical approach to enable intelligent applications is cloud-centric, meaning that the intelligence (a home assistant) is hosted in the cloud infrastructure, and the sensor data collected by some IoT devices (a microphone array and a temperature sensor) flow from the cyber-physical-system until reaching a remote endpoint to be processed. Finally, the correct command is transmitted to the IoT actuator (a radiator thermostat). Alternative approaches to this are possible, for instance, by considering a more dynamic and configurable intermediate layer placed between the IoT and the Cloud sides, usually dubbed as the Edge layer. Generally, a configurable edge layer reduces the required bandwidth and latency and improves users’ privacy. Moreover, if portions of the application intelligence could be hosted in this layer, the IoT device lifetime would be enlarged. However, reconfiguring and deploying an end-to-end processing flow that involves the three aforementioned architectural layers poses major challenges. Select a more efficient detection algorithm from a rich machine learning algorithms library and pushing the “deploy” button of an application dashboard to see the selected algorithm up and running more effectively (according to a given metric) on my smart home devices is still a dream, in most of the cases. Moreover, depending on the hardware capabilities, the application requirements in terms of bandwidth and latency, and the accuracy required for the machine learning task to execute, different end-to-end configurations are possible, all sub-optimal and possibly non-dominated in the Pareto meaning. The subject of this Ph.D. is to investigate and propose novel optimization and assessment methodologies to efficiently sample such a complex design space in target application sectors such as home, industry, manufacturing, farming, etc. The reference technological environment covers (but is not limited to) embedded device software engineering (micropython, mbed OS, C languages and dialects, etc.), machine learning frameworks deployable on tiny devices (tinyML, TensorFlow lite, etc.), edge-based frameworks (eclipse Kura, edgeX Fundry, etc.) and cloud-based IoT platforms and services with AI support.

The EU promotes the creation of data spaces in order to maximize the impact that gathered data can have on society and the environment. At the same time municipalities and regional governments push for new acquisitions of multimodal geospatial data on their territories (cities, forests, rural areas, etc.). However, the integration of such data at different resolution, radiometry and acquisition systems is one of the biggest obstacle for proper data exploitation. Therefore the goals of the proposed PhD are:
(i) to study, develop and validate innovative solutions to fuse geospatial data (such as 3D point clouds, multi/hyperspectral orthoimages, etc.) of the built/vegetated environment ad extract metric information;
(ii) to conduct research on novel and efficient algorithms for 3D data semantic segmentation and classification using integrative AI approaches that can effectively replace traditional hand-crafted methods to ultimately improve performance and interpretability, handle unbalanced classes, ease deployment and foster scalability;
(iii) to analyze, realize and demonstrate new methods to create digital twins of our environment using multimodal geospatial data.

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

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

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

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

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

One of the pillars of healthcare digital transformation focuses on the integration of AI-based dialogue solutions within the clinician-patient relationships with the aim of monitoring and/or supporting them toward the achievement of healthy functional status.
The advent of large language models (LLMs) changed the research paradigm in natural language processing (NLP) and it put the basis for new challenges.
Two of the most main issues of such LLMs are identified by the analysis of how they are ethical and sustainable.
By “ethical”, we mean their capability of both building models that are not biased (in a broader sense) and generating content that is controlled with respect to the task they are used for and the environment in which they are deployed.
Instead, as “sustainable”, we mean the possibility of training and fine-tuning LLMs with costs that may be afforded by any academic entity.
In this Ph.D., the candidate will investigate (i) how to design dialogue systems in a sustainable way by performing fine-tuning operations on existing LLMs; and, (ii) how to inject grounded knowledge into LLMs to enable ethical and bounded conversations between the AI-based system and the target users.

Thanks to the increasing digitalisation of contemporary organizations, event data about the execution of processes are continuously collected. Process intelligence aims at transforming these data into insights into how processes are executed in reality.

In spite of the reported effectiveness of state of the art approaches for process intelligence, an effective intelligent usage of execution data is still reported to be one of the key challenges of today's strategic management. Indeed, AI is becoming increasingly central to realise a view that shifts from descriptive and reactive predictive analytics to proactive prescriptive analytics, able to improve the impact, sustainability, and relevance of data-enabled decisions. Realising this vision requires addressing several foundational challenges: (i) the representational descriptive challenge, which arises from the multiperspective representation of knowledge for the specific domain containing time, resources, data objects, costs ...; and (ii) the predictive and proactive challenge, which demands the usage of data in an explainable manner and to realise recommender systems able to deal with the temporal dimension of data so as to support decision making. It also calls for an integrative approach that leverages the power and flexibility of Machine Learning techniques, combines the (implicit) knowledge contained in the data with the explicit (and often de facto or legally binding) rules governing the process behaviour, and resorts to different reasoning techniques. The PhD project will focus on the development of new AI based techniques able to deal with some of the challenges above and their application in several domains, e.g., the healthcare one.

Extracting information from clinical documents in a multilingual perspective.

Investigation of the effectiveness of cognitive-behavioral psychological interventions based on virtual reality and artificial intelligence.

The main goal of the research is to build an embodied system capable of interacting with an unknown environment and continuously learn about such an environment by exploiting commonsense knowledge. The experimental evaluation of the approach should involve both simulated environment and real environments. Aspects of sim2real transfer will also be considered.

Performance tests of Proton conductive ceramic cell for FC/EL particularly for Compression separation (from N2, cracking NH3, H2/H2O) with dedicate Durability test or AST on PCC

Research focus will be on the development and the validation of multiphysics-multiscale tools that will accelerate the optimal design and management of redox flow batteries, a technology particularly suited for long-duration energy storage. Flow batteries are a complex system and a multitude of physical phenomena involving different scales (from micro to system) need to be considered to forecast cell and battery system performance and optimize the design of each component.