Reserved topic scholarships | Doctoral Program - Information Engineering and Computer Science

Reserved topic scholarships

Department of Information Engineering and Computer Science

A1 - Generalized Category Discovery (project HE RIA AI4TRUST; GA n. 101070190; CUP E63C22003900006) (1 grant)

Generalized Category Discovery (GCD) involves learning from known categories to accurately categorize datasets that encompass both known and unknown categories. Currently, GCD tasks are limited to a single visual modality but this project should advance GCD by leveraging multimodal data and by replicating human-like categorization abilities that typically integrate multiple modalities, such as visual, auditory, and textual elements, in recognizing subjects. Concretely the focus should be on several aspects: 1) Theoretical exploration of GCD; 2) Development of multimodal GCD benchmarks; 3) Synergy of multimodal Category Discovery abilities.

Contact: Niculae Sebe niculae.sebe [at]

​​​​A2 - Generative models for fake news analysis (project HE RIA AI4TRUST; GA n. 101070190; CUP E63C22003900006) (1 grant)

The PhD project will focus on developing novel techniques to discern and combat the proliferation of misinformation. By leveraging generative models like diffusion models, the research will explore methods to detect and generate fake news content, with special emphasis on multimodal contents. Additionally, the project will delve into the creation of countermeasures, such as generating trustworthy content or developing robust detection algorithms to identify fake news across various media platforms.

Contact: Elisa Ricci e.ricci [at]

​​​​​​B1 - Imitation learning of robotic manipulation tasks (project UE HE INVERSE Saveriano, CUP E63C23001600006) (1 grant)

The PhD student will work in imitation learning in robotics. The Project is sponsored by the Horizon Europe INVERSE probject. The problem is observing humans while they perform operations (e.g., in a manufacturing or a medical scenario), reconstructing the logical flow of these operations, and translating each operation in a way that a robot can execute. The PhD student will set the theoretical groundwork for imitation learning and develop some concrete applications using the robotic platforms used for the project.

Contact: Luigi Palopoli luigi.palopoli [at] Matteo Saveriano matteo.saveriano [at]

​​​​​​D1 - Artificial intelligence and machine learning for the automatic analysis of planetary radar data (project: ASI JUICE RIME 2021; CUP: F65F21000950005) (1 grant)

Space missions for the exploration of planets and celestial bodies in the Solar system are very important for the huge scientific and technological return associated with them. Some of the most challenging science objectives of these missions are related to the analysis of sub-surface processes that are of crucial importance for many different implications they have. 
The research activities of this grant are related to planetary radar sounders, which are instruments for the study of the subsurface of the planets of the Solar system. These radars operate from satellite platforms and acquire images related to the subsurface of celestial bodies that can results in groundbreaking science results. The activity will be focused on the development of methodologies for the processing and the automatic analysis of these data. Special emphasis will be given to methodologies that exploit the most recent developments in the framework of machine learning and signal processing.
Research will be developed at the Remote Sensing Laboratory ( and will be related to the Radar for Icy Moon Exploration (RIME) on board of the JUpiter ICy moons Explorer (JUICE) of the European Space Agency (see for more details on the mission).

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at]

​​​​​​D2 - Artificial intelligence and signal processing for planetary radars (project ASI EnVision ph.B1, Accordo n.2022-23-HH.0; CUP F63C22000650005) (1 grant)

The research of this grant is related to the use of artificial intelligence and machine learning techniques for the analysis of data acquired by planetary radar sounders. Radar sounders operate from satellite platforms and acquire data related to the subsurface of celestial bodies that can results in groundbreaking science results. This activity will be developed in the framework of the EnVision mission of the European Space Agency (ESA) (for more information refer to and in particular on the Sub-surface Radar Sounder (SRS) on board the mission. SRS has the objective to investigate the shallow Venus subsurface (up to few hundred meters) to reveal its mysteries by studying the tectonic, volcanism, impacts, relation between surface and subsurface features, etc.
The PhD research activity will include at least one of the following topics:
•    Novel methodologies and techniques based on machine learning and signal processing for the analysis radar sounder data.
•    Simulations for the analysis of the performance of the radar versus different Venus scenarios by integrating traditional simulation techniques with machine learning approaches.
•    Test campaigns with ground penetrating radar from drone/helicopter on terrestrial sites considered as analogous of Venus subsurface.
Research will be developed at the Remote Sensing Laboratory (

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at]

​​​​​​D3 - Deep learning for the automatic analysis of satellite remote sensing images (project: ESA HRLC_CCI+ 2023 - Bruzzone; CUP E63C23002010006) (1 grant)

The automatic analysis of image time series acquired by Earth Observation satellites is crucial for many different applications (e.g., precision farming, forestry, analysis of urban areas, monitoring of natural disasters). One of the most important applications is related to the monitoring of the land cover in relation to climate changes. In this context, even if artificial intelligence and machine learning have been widely used for the automatic analysis of remote sensing data, there are still many methodological challenges and application issues that should be addressed in the analysis of long time series of satellite data. 
The research activities of this grant are related to the development of novel methodologies based on deep learning for the automatic analysis of time series of optical (multispectral) images acquired by Earth Observation satellites. The research will be focused on the problems of the automatic classification (sematic segmentation) and change detection in multispectral images acquired by different satellite systems. The activity will consider methodological research (for the development of novel methods) and the related application to real world scenarios.
Research will be developed at the Remote Sensing Laboratory ( and will be linked to the High Resolution Land Cover project developed under the leadership of RSLab in the framework of the Climate Change Initiative of the European Space Agency (

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at]

​​​​​​D4 - Development and clinical translation of innovative ultrasound localization microscopy techniques (1 grant)

This PhD position is focused on the development and clinical translation of innovative ultrasound localization microscopy techniques, also designed for innovative and dedicated monodisperse Ultrasound Contrast Agents (UCAs). Research activities (in collaboration with Solstice Pharmaceutics and CNR) will include:
- characterization of monodisperse UCAs response to ultrasound waves;
- design and fabrication of 3D printed phantoms mimicking the vascular environment typical of pathologies such as cancer and Alzheimer (including accurate control on geometrical and dynamic features);
- development and testing of novel imaging strategies, vessel reconstruction algorithms, and image analysis techniques to be tested experimentally, pre-clinically and clinically (also applicable to photoacoustic imaging);
Research activities will be conducted both at the Ultrasound Laboratory Trento (Italy) and at Solstice Pharmaceutics (the Netherlands). The selected candidate will thus have the opportunity to experience an international working environment as well as gain academic and industrial experience. Additional allowance is available to support the periods abroad. The preferred candidate has experience in biomedical imaging, signal processing, image formation, image analysis, and machine learning."

Contact: Libertario Demi libertario.demi [at]

Fondazione Edmund Mach - Department of Information Engineering and Computer Science

D5 - Automatic techniques for forest monitoring with time series of satellite remote sensing (1 grant)  

The role of climate change on forests and on their dynamics is becoming more and more evident. It is important to continuously monitor forest areas to understand the impacts of climate change and assist policymakers and stakeholders in updating forest management policies.
Earth Observation satellites are widely used for forest monitoring. The automatic analysis of these data is crucial for their use in an operational scenario. While several methods have been developed for these applications, there are still many methodological challenges especially when considering the monitoring of subtle and continuous dynamics such as insect infestations.
The research activities of this Phd project aim to develop novel methods for the automatic analysis of image time series for forest monitoring, including the analysis of vegetation dynamics, forest structure, and disturbance events. Specific attention will be devoted to the analysis of time series data from the Sentinel-2 and Landsat-8, as well as other relevant data sources (e.g., Planet constellations, PRISMA).
The activities will be carried out at the Forest Ecology unit of Fondazione Edmund Mach and at the Remote Sensing Laboratory of the Università degli Studi di Trento.

Contact: Damiano Gianelle damiano.gianelle [at]  Lorenzo Bruzzone lorenzo.bruzzone [at]

Fondazione Bruno Kessler (FBK)

A3 - Advancing State-Of-The-Art in Multi-Modal Learning with Innovative Neural Architectures for Multi-Lingual Speech Processing (1 grant)

In recent years, Multi-Modal Learning (MML) has garnered significant attention, driven by the increasing availability of vast multimodal datasets and the development of robust internet services accessible across various devices. Current research predominantly emphasizes the imperative to harness deep learning techniques, capitalizing on existing foundational models applicable across diverse domains, and customizing them for specific tasks within the MML framework.
The primary objective of this thesis is to advance the state of the art in MML by delving into cutting-edge neural architectures and learning approaches. This involves the exploration of innovative methods, potentially combining different modalities such as voice and gestures. The aim is to enhance the performance of existing single-modal systems, particularly in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP).
A pivotal focus will be on investigating recent audio foundation models, specifically those designed for multi-lingual speech recognition, voice conversion, and speech generation to facilitate data augmentation. The overarching goal is to develop models that exhibit effectiveness across various speech tasks, even when confronted with limited data or constrained computation resources. Furthermore, through the integration of advanced audio generation techniques, the study seeks to bolster the multimodal capabilities of the overall system. This enhancement enables more effective creation of synthetic data for model training and development.
The outcomes of this research are anticipated to contribute significantly to the development of highly competitive services tailored to the demands of evolving multimodal application scenarios.

Contact: Alessio Brutti brutti [at]

A4 - AI models for human mobility (1 grant)

Predicting individual and collective human behavior is crucial to address complex societal challenges. Recent research has focused on deep learning models for forecasting future behavior. While these models achieve impressive results, they face limitations: 
limited generalizability, low interpretability, and difficulties in geographic transfer. This PhD project aims to design the next generation of computational models for understanding individual and collective human behavior. Social science research on social learning, 
collective intelligence, and crowd wisdom identifies potentially generalizable behavioral patterns. Additionally, recent advancements in AI offer foundation models capable of reasoning and generalization.
The ideal candidate possesses a strong interest in a multidisciplinary approach encompassing machine learning (deep learning and foundation models), social sciences, urban mobility, and related fields. The ultimate goal is to contribute to the development of the first foundation model for human behavior. 
The collaborative nature of the project fosters engagement with leading national and international universities, creating a dynamic research environment.

Contact: Bruno Lepri lepri [at]

A5 - Artificial Intelligence for Tiny, Connected Devices: Enabling Learning and Inference on Resource-Limited Networked Embedded Systems (1 grant)

"he Internet of Things (IoT) paradigm proposes massive increases in multimodal data creation on tiny devices located at the far edges of the computing infrastructure. Despite the resource constraints of these devices, communication constraints require the data they produce to be locally processed before transmission. Further, due to the increasing complexity of the data generated, machine learning techniques are being successfully applied at the far edge through so-called TinyML. While most TinyML techniques focus on the individual device, increasingly these devices are both networked together and/or connected to larger cloud or swarm based infrastructures, introducing challenges for managing communication and coordination, both in data collection and usage as well as regarding elements of distributed training and continuous learning. Research into these challenges requires innovation in i) applying novel techniques such as distillation, hardware aware scaling and neural architecture search to implement orchestratable TinyML algorithms, ii) innovative communication, including optimizing the low power communication network connecting edge devices to the infrastructure, and iii) orchestration techniques required for ML-enhanced edge devices to participate in complex distributed applications formed of heterogeneous devices. 
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 machine learning at the very edge.

Contact: Amy Lynn Murphy murphy [at] Elisabetta Farella efarella [at]  

A6 - Cooperative Large Language Models (1 grant)

Nowadays, foundation models have shown remarkable capabilities in generating text, images and videos, rich world knowledge, and some complex “reasoning” skills. However, these models are still passive models (e.g., action-oriented aspects of intelligence are not leveraged), static, data- and computation-expensive, they often confabulate, and are difficult to align with human values.
This PhD project aims at making multimodal foundation models capable of interactions with other agents (e.g., enabling cooperative capabilities) and of interactions grounded in the world, enabling counterfactual reasoning and causality abilities in foundation models as well as enforcing their alignment with human intentions and values. The PhD candidate is required to have previous experience in working with deep learning algorithms, a strong interest on transformers’ architecture, graph neural networks, multimodality, and cooperative and embodied AI. The selected student will be able to collaborate with the ELLIS network and being part of the ELLIS PhD program (if selected) as well as with top universities and research centers such as MIT, Max Planck, etc.

Contact: Bruno Lepri lepri [at]

A7 - Enhancing Biodiversity Conservation with AI-based Geospatial Technologies (1 grant)

Biodiversity is under threat from habitat loss and fragmentation, climate change, extreme land use and invasive alien species. Biodiversity monitoring for conservation purposes using geospatial data has seen some progress in recent years. Traditional survey methods are time-consuming, labour-intensive and require skilled staff. Surveying can be challenging, especially to keep track of rare or elusive species living in inaccessible or dangerous areas. Photogrammetric and LiDAR data has led to important advancements and impacts on ecosystem understanding as they enable precise assessments of habitat structures, mapping of species distributions or ecosystem dynamics, all essential information for conservation efforts. Nonetheless, better objective processes and monitoring solutions, with new tools to enhance and enrich current practices and assist ecologists, are needed.
Therefore, the goal of the interdisciplinary PhD is to:
(i) create and validate processes, based on 3D remote sensing data (airborne/drone monochromatic/multispectral LiDAR, photogrammetric point clouds, aerial/drone hyperspectral images, etc.) and AI methods, to locate and study various species, either animal or vegetal
(ii) use ground robotics platforms and sensors for terrestrial biodiversity exploration and monitoring in challenging environments
(iii) find biodiversity patterns, through multimodal data analytics, detecting hotspots of current issues, their trends and emerging threats
(v) combine/fuse 3D data to cross-validate the methods
(vi) find new links within geographical data between animals and vegetation.

Contact: Fabio Remondino remondino [at]

A8 - Foundational and language models for 3D scene understanding (1 grant)

3D scene understanding is an area of vision research with applications ranging from augmented reality to autonomous navigation. This PhD position is focused on research into foundational models for 3D scene understanding. 3D scenes can be created from image collections (Structure from Motion, Simultaneous Localisation and Mapping), thus allowing for the extraction of foundational representations from images and their transfer to the 3D domain using pixel-to-point correspondences. These representations can then be interacted with via language model prompting. However, these representations have been optimised for 2D reasoning. The PhD candidate will be tasked with exploring novel approaches to disentangle object-level information in the 2D domain and to fuse it in 3D, thereby enabling 3D reasoning capabilities.

Contact: Fabio Poiesi poiesi [at]

A9 - Integrating human-like understanding into Large Multimodal Models (1 grant)

Publicly available multimodal datasets often consist merely of text captions paired with images, text with audio, or audio with images, while also merely providing descriptions of the images without establishing any deep-level connection between the text content and the visual or auditory content. Such formats fail to reflect the complexity of human perception, such as how we process images, listen to audio, or comprehend text. The PhD candidate will be tasked with exploring new modalities that more accurately reflect human vision and attention as they align with verbal descriptions. This initiative aims to foster a profound understanding of how visually perceived content (including images, videos, and text) is processed and understood by humans. Achieving this understanding will allow us to advance in the comprehension capabilities of Large Multimodal Models, while enhancing their ability to interpret and interact with a wide range of data.

Contact: Fabio Poiesi poiesi [at]

A10 - Resource-efficient Foundation Models for Automatic Translation (1 grant)

The advent of foundation models has led to impressive advancements in all areas of natural language processing. However, their huge size poses limitations due to the significant computational costs associated with their use or adaptation. When applying them to specific tasks, fundamental questions arise: do we actually need all the architectural complexity of large and - by design - general-purpose foundation models? Can we optimize them to achieve higher efficiency? These questions spark interest in research aimed at reducing models’ size, or deploying efficient decoding strategies, so as to accomplish the same tasks while maintaining or even improving performance. Success in this direction would lead to significant practical and economic benefits (e.g., lower adaptation costs, the possibility of local deployment on small-sized hardware devices), as well as advantages from an environmental impact perspective towards sustainable AI. Focusing on automatic translation, this PhD aims to understand the functioning dynamics of general-purpose massive foundation models and explore possibilities to streamline them for specific tasks. Possible areas of interest range from textual and speech translation (e.g., how to streamline a massively multilingual model to best handle a subset of languages?) to scenarios where the latency is a critical factor, such as in simultaneous/streaming translation (e.g., how to streamline the model to reduce latency?), to automatic subtitling of audiovisual content (e.g., how to streamline the model without losing its ability to generate compact outputs suitable for subtitling?).

Contact: Matteo Negri negri [at] Luisa Bentivogli bentivo [at]

A11 - Understanding 3D heritage with ontologies and AI (1 grant)

Point clouds are nowadays an indispensable tool in the heritage field, but their actual usage by non-experts engaged with preservation and restoration challenges is often very limited, due to low explainability, human-readability, accessibility and data integrability issues. All of these issues fall under the conceptual umbrella of “understandability”.
AI-based approaches particularly suffer these problems, but, as point clouds, they are an indispensable tool for digital heritage. Other approaches, e.g. based on 3D ontologies, could support understanding aspects but they have been limited addressed.
Therefore the goals of the proposed PhD are:
(i) To study, develop and validate generalisable ontology-based approaches to facilitate the query and use of large and complex 3D heritage point clouds by means of rules able to infer properties and characteristics of a surveyed scene
(ii) To conduct research on novel ways to integrate formal ontologies and AI-based methods to support explainability 
(iii) To integrate LLM and NLP models to support 3D heritage understanding 
The successful candidate is supposed to have a good ability to connect ICT/AI solutions with heritage needs, along with the agility to successfully prototype innovative, reliable and replicable software solutions.

Contact: Fabio Remondino remondino [at]

B2 - Advancing Edge Computing and IoT through MLOps Innovations (1 grant)

The proliferation of Internet of Things (IoT) devices and the increasing reliance on edge computing paradigms have ushered in a new era of distributed computing, where processing occurs closer to the data source rather than in centralized data centers. This shift promises to reduce latency, minimize bandwidth usage, and ensure data privacy and sovereignty. However, it also introduces significant challenges in managing, deploying, and maintaining machine learning (ML) models across a vast, heterogeneous, and geographically dispersed infrastructure. This doctoral research aims to address these challenges by advancing the integration of Machine Learning Operations (MLOps) practices within edge computing and IoT environments, facilitating the seamless deployment, monitoring, and management of ML models at the edge.

Contact: Massimo Vecchio mvecchio [at]

B3 - Offloading Security to Programmable Data Planes (1 grant)

Programmable Data Planes (PDPs) offer the ability to customise and control the processing of network packets, either within network devices such as routers, switches, and SmartNICs, or within end-host machines, through technologies like eBPF (extended Berkeley Packet Filter), or other programmable frameworks. PDPs empower developers to define and implement customised packet processing logic, spanning from fundamental packet filtering and forwarding to more sophisticated tasks like load balancing, network virtualisation, and security enforcement.
While they promise to deliver enhanced flexibility and performance to processes and services, the capabilities of PDPs are still largely under exploration, proof, and assessment. As a matter of facts, due to design and implementation decisions, they are often restricted in the range and types of operations they can perform on packets. This thesis aims to delve into novel and advanced methodologies for offloading complex tasks, particularly those focused on security such as cryptography, traffic analysis, and filtering, onto PDPs, with the objective of striking the right balance between performance and complexity. The candidate is expected to analyse alternative solutions where tasks can be either entirely or partially offloaded and conduct experimental assessments, comparing the outcomes against legacy approaches.

Contact: Domenico Siracusa dsiracusa [at]

B4 - Opportunistic Monitoring for Cloud-to-Edge Environments (1 grant)

The drive to establish a cloud-to-edge continuum, spanning multiple heterogeneous compute regions, is progressively eroding the boundaries of security perimeters, calling for a zero-trust approach to security, where nothing, not even inside an organisation’s network, can be implicitly trusted. In such a scenario, a resource- and energy-efficient, opportunistic monitoring of users, services, platform, and infrastructure becomes paramount to enhance management and security in cloud-to-edge environments. 
Monitoring and auditing have received increasing attention from research and industry in the recent years. Crucially, novel technologies have emerged to programmatically gather information from network flows, system calls, and other sources. These technologies, including eBPF (extended Berkeley Packet Filter) and P4, fall under the umbrella of the so-called programmable data planes. 
The objective of this PhD endeavour is to design, implement and evaluate novel monitoring solutions capable of opportunistically diving into the appropriate depth of data collection, defining the right mix of data and user/control plane functions and their location. Additionally, they should be programmatically tailored to serve security and data analysis applications, while being suitable for dynamic scaling and orchestration. The overarching goal is to contain their footprint while delivering the required information effectively.

Contact: Domenico Siracusa dsiracusa [at]

B5 - Robustness of Intrusion Detection Systems against Adversarial Machine Learning attacks (1 grant)

A Network Intrusion Detection System (NIDS) serves as the initial line of defence against network attacks that threaten the integrity of data, systems, and networks. Over recent years, Machine Learning (ML) algorithms have been increasingly used in NIDSs to detect malicious traffic due to their remarkable accuracy in identifying malicious network activity. 
Nevertheless, ML algorithms are susceptible to Adversarial Machine Learning (AML) attacks, which aim to evade the NIDS with small perturbations of the attack network traffic. This vulnerability has particularly severe consequences, as adversarial attacks pose a substantial threat to overall network security. 
While the majority of current research in the field of AML has been directed towards computer vision tasks like image classification and object recognition, there has been a notable increase in interest and activity within the cybersecurity domain. Nevertheless, several challenges persist in this domain, encompassing both performance-related issues and the practicality of applying these methods to real-world scenarios. 
The primary objective of this PhD scholarship is to conduct cutting-edge research in the field of AML with a focus on enhancing cybersecurity defences. The selected candidate will explore innovative techniques and methodologies to detect, prevent, and mitigate AML attacks, thereby improving the robustness and resilience of ML-based cybersecurity systems.

Contact: Domenico Siracusa dsiracusa [at]

 C1 - Advancing Agricultural Sustainability through Data Intelligence (1 grant)

This initiative contributes to global food security and environmental sustainability, fostering a collaborative and innovative research atmosphere focused on integrating data science and artificial intelligence (AI) with agriculture. It addresses urgent challenges such as resource management, crop yield optimization, and the impacts of climate change. As the global demand for food increases, this scholariship promotes the adoption and creation of technologies and methodologies to boost agricultural efficiency and sustainability. The successful candidate will engage in developing innovative, data-driven methods for sustainable agricultural practices. Additionally, he/she will explore interdisciplinary research, utilizing remote sensing, machine learning, and predictive analytics to produce actionable insights aimed at enhancing agricultural productivity and resilience. This scholarship aims to cultivate a new breed of researcher, adept at leveraging data intelligence for agricultural progress, with a focus on optimizing water usage, enhancing crop resilience, and implementing sustainable farming practices.

Contact: Massimo Vecchio mvecchio [at]

C2 - Formal Methods for Digital Twins (1 grant)

Digital twins are dynamic and self-evolving models that simulate a physical asset and represent its exact state through bi-directional data assimilation. They employ Artificial Intelligence (AI) data-driven and symbolic techniques to provide state synchronization, monitoring, control and decision support. 
This project will build on the results of the ongoing ESA-funded project ExploDTwin (""Digital Twin for Space Exploration Assets""), which aims at integrating the DT into the ESA infrastructure to support online space assets operations with functionalities such as planning, what-if-analysis, fault detection, diagnosis and prognosis. To this end, ExploDT introduces a model-based design methodology that allows to seamlessly integrate engineering methods and AI techniques into a cohesive DT framework. 
The PhD student will investigate new formal methods to analyze the DTs by integrating model checking, automated theorem proving, simulation, and machine learning. Different aspects of the DTs will be considered including temporal properties for validation, monitorability and diagnosability. The new methods will be implemented and evaluated on space related benchmarks derived from ExploDTwin results.

Contact: Stefano Tonetta tonettas [at]

C3 - Formal methods for embedded software (1 grant)

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.

Contact: Alberto Griggio griggio [at]

C4 - Formal methods for industry (1 grant)

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.

Contact: Marco Bozzano bozzano [at] Alberto Griggio griggio [at]

C5 -Testing methodologies for complex parametric systems (1 grant)

The growing complexity of software systems requires the development of new methods and tools to design and test software systems characterized by high variability in the space of possible functional configurations and possible release architectures. The objective of this doctoral thesis is to explore new approaches to testing, verification and validation of this type of complex systems involving the joint use of model-based and artificial intelligence techniques such as optimization, planning and machine learning.

Contact: Angelo Susi susi [at]

D6 - Advanced methods for the analysis of remote sensing time series (1 grant)

The recent Earth Observation missions like (ESA Copernicus - Sentinels, ASI PRISMA and COSMO-SkyMed, and future IRIDE constellation) make available databases of long, dense and worldwide image time series. The data have complex spatio-spectro-temporal behaviors and variability, and they show irregularities and misalignments.
Candidates will be requested to develop novel methodologies within artificial intelligence framework (machine learning, deep learning, pattern recognition, etc.) for effectively and efficiently process image time series for semantic segmentation, target detection and change detection across multiannual series of data. 
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/Data Science, Mathematics or equivalents;
•    background in artificial intelligence, image/signal processing, remote sensing, passive/active sensors.

Contact: Francesca Bovolo bovolo [at]

D7 - Advanced radar sounder data processing and information extraction (project ASI-INAF n. 2023-6-HH.0 JUICE-RIME-E — CUP no. F83C23000070005) (1 grant)

In the context 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, we seek for candidates willing do develop methodologies for radar sounder data processing (data enhancement, semantic segmentation, denoising, content based retrieval, target detection, multitemporal analysis, etc.). The outcome of this activity will contribute in improving the understanding of the subsurface of planetary bodies, the correlation to history and climate as well as to a better understanding of the Earth.
The candidate will be requested to design and develop novel methodologies within artificial intelligence framework (machine learning, deep learning, pattern recognition, etc.) for effective information extraction from radar sounder data.
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/Data Science, Mathematics or equivalents;
•    knowledge in artificial intelligence, image/signal processing, remote sensing, radar remote sensing.
This scholarship is funded by project ASI-INAF n. 2023-6-HH.0 JUICE-RIME-E - “Missione JUICE - Attività dei team scientifici dei Payload per Lancio, commissioning, operazioni e analisi dati” — CUP no. F83C23000070005

Contact: Francesca Bovolo bovolo [at]