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

Reserved topic scholarships

Department of Information Engineering and Computer Science

A1 - Adaptive Multimodal conversational agents (1 grant)

Thanks to the research on multi-task learning and on transferability, the current trend in natural language understanding is to build universal models: models pre-trained on several tasks and fine-tuned on a downstream task to which the acquired conceptual knowledge and skills are transferred. These steps are very important contributions to AI, and in particular to the development of conversational systems, but universal models do not model human intelligence diversity. We believe it is important to work with conversational models which have different prior experiences and use different conversational strategies. With the progress on modelling vision and language interaction, current dialogue models are able to deal with visually grounded language. However, they are not able to adapt their language use to the interlocutor, hence they miss an essential ability to be engaged in effective communication. ADAPT aims to address this problem.

To accomplish our overall goal, the research agenda of ADAPT capitalises on recent advances in  the development of multimodal  models; on the availability of multimodal datasets for referential  visual games with textual interactions among players; and on methods developed recently within the research on emergent communication in referential  games.  To these active and promising fields, we will add a fundamental missing component, namely the view about how humans take community mutual knowledge into account and manage to adapt to each other through conversations. 
The scholarship is supported by Amazon Alexa and the work will be run in collaboration with their research group.

Contact: Raffaella Bernardi raffaella.bernardi [at]

A8 - Learning to adapt with Geometric Deep Learning (MUR PRIN 2020 - CUP E63C22000390001- Prot. 2020TA3K9N) [1 additional grant ]

While modern AI systems exhibit astonishing performance reaching human-level capabilities in different applications, they are still severely lacking human abilities of learning incrementally and continuously, as well as quickly generalizing from few examples and adapting to new and unseen environments. Addressing all these aspects is mandatory for applying AI in real world setting. Individually, these challenges have been tackled by the scientific community within the research areas of Continual Learning, Domain Adaptation, Few Shot Learning and Novel Class Discovery. However, to date, no unified solutions for creating AI systems that seamlessly tackle these challenges, as humans naturally do, exist. In this PhD project novel theoretical and computational frameworks to simultaneously discover, represent and update knowledge in AI systems, will be investigated exploiting recent advances from Geometric Deep Learning. This grant is funded by the project MUR PRIN 2020 - LEGO.AI: LEarning the Geometry of knOwledge in AI systems, CUP E63C22000390001- Prot. 2020TA3K9N

Contact: Elisa Ricci e.ricci [at]

B1 - Automated Program Repair for bugs and security vulnerabilities  (1 grant)

Automated software and configuration repairs aims at recovering from security vulnerabilities and is a promising way to support systems with a  continuous developement and deployment pipeline. Artificial Intelligence (AI) methods, such as machine and deep learning, evolutionary algorithms, and explainability techniques, can be leveraged to: (i) enhance state-of-the-art testing techniques by combining knowledge extracted from different sources (e.g., code repositories, requirement specifications, system executions); and (ii) increase testing automation (iii) provide automated or at least computer aided repairs.
Within the Ph.D., the candidate will work (a) by learning and combining knowledge from heterogeneous system artifacts and (b) by comparing and enriching these techniques with AI methods. The Ph.D. work will involve theoretical, methodological and empirical aspects, ranging from the capability to design new solutions up to the development of new tools and their experimental validation.
The work will be partially carried out in the context of the AssureMOSS H2020 project ( and in collaboration with some of its industry partners most notably SAP and FrontEndArt. 

Contact: Fabio Massacci fabio.massacci [at]

B2 - Testing and Explainability for bugs and security vulnerabilities  (1 grant)

Software testing aims at discovering and preventing bugs and security vulnerabilities and is a promising way to verify systems with a  continuously evolving systems. Artificial Intelligence (AI) methods, such as machine and deep learning, evolutionary algorithms, and explainability techniques, can be leveraged to: (i) enhance state-of-the-art testing techniques by combining knowledge extracted from different sources (e.g., code repositories, requirement specifications, system executions); and (ii) increase testing automation (iii) provide explainable characterization of AI outcomes.
Within the Ph.D., the candidate will work (a) by learning and combining knowledge from heterogeneous system artifacts and (b) by comparing and enriching these techniques with AI methods. The Ph.D. work will involve theoretical, methodological and empirical aspects, ranging from the capability to design new solutions up to the development of new tools and their experimental validation.
The work will be partially carried out in the context of the AssureMOSS H2020 project ( and in collaboration with some of its industry partners most notably SAP and FrontEndArt 

Contact: Fabio Massacci fabio.massacci [at]

B3 - HW/SW co-design of trusted services (1 grant)

The PhD grant is related to the study, design, implementation and validation of novel trusted services typically running inside trusted execution environments (TEE) to complement and/or enrich the existing ones (e.g., secure boot, secure attestation, cryptographic functions, etc.). Among the possible ones, are the services to ensure the protection of the machine learning models often embedded in hardware, device id generation and protection, biometric information protection. These services will be designed, implemented, and verified leveraging open design specifications. The PhD include also the study of the current TEE design in order to assess the root causes behind the numerous deficiencies affecting existing TEEs.

Contact: Bruno Crispo  bruno.crispo [at]

B4 - Resilient AI-Based Self-Programming [PRIN 2020 - CUP  E63C22000400001; Prot. 20203FFYLK] (1 grant)

This PhD aims at developing a core theory and algorithms for resilient self-programming, i.e, to define mechanisms to enable agents to act in an informed and intelligent way in their environment, by changing autonomously the way they behave as a consequence of the information they acquire from the external world and exchange with the humans operating therein. The study will focus on extending existing approaches to AI Planning and LTL/LTLf synthesis by i) enriching the computed strategies with fault-tolerant capabilities, ii) considering several models at synthesis and execution time leveraging the most appropriate model depending on the observed contingency at execution time; iii) integrating reinforcement and model learning to enable for determining tolerant strategies that work in a reference model plus variations. The theoretical framework is complemented by the realization of prototype supporting tools as well as practical applications in selected realistic scenarios. This grant is funded by the project MUR PRIN 2020 - RIPER - Resilient AI-Based Self-Programming and Strategic Reasoning - CUP E63C22000400001.

Contact: Marco Roveri  marco.roveri [at]

B5 - Formal framework for Trustable IoT applications (1 grant)

This PhD aims at defining a formal specification framework for the design and deployment of trusted IoT applications considering all the different facets and by attaching to the IoT applications a “certification manifest”. The study will consist in formally defining the concepts of safety and assurance for IoT applications leveraging on temporal logic specifications complemented with security concepts, and adaptability of existing frameworks and verification tools to address the identified verification problems. The theoretical framework will be complemented with implementation activities to develop/adapt different verification tools for the different verification activities. The work will also consider i) studies aiming at adapting existing translation validation techniques to ensure the verification of safety and security policies for the considered IoT application when deployed in a specific trusted execution environment leveraging SMT techniques; ii) AI techniques to reconstruct from the binary code high-level functions and control flow graph to enable the verification of safety and security properties to assure security policies are not violated by updates/patches.

Contact: Marco Roveri  marco.roveri [at]

B6 - Human aware motion planning for mobile robots and manipulators (1 grant)

The PhD candidate will work in the area of robot motion planning and task planning. Specifically, she/he will explicitly account for the presence of humans adapting the generated plan to the dynamic changes determined by the human actions. The student will study the psychological and safety implication of moving nearby humans.

Contact: Luigi Palopoli luigi.palopoli [at]

C1 - Innovative Education in Computer Science (1 grant)

The PhD research grant has the objective of exploring, designing, executing and evaluating highly qualified educational initiatives for Computer Science, and more in general, Engineering students in the field of innovation and entrepreneurship education. Topics related to innovative teaching, based on active-based, participatory and challenge-based learning, will be addressed in collaboration with ongoing European projects. Ideal candidates will have (or will aim to develop) an interdisciplinary profile, combining computer science with the disciplines belonging to digital innovation and entrepreneurship area.

Contact: Maurizio Marchese maurizio.marchese [at]

C2 - Participatory Artificial Intelligence (1 grant)

The relevance of AI and Machine Learning is posing new challenges to Participatory Design, from both a methodological and application perspective. This
research topic is connected with what is referred as Explainable AI, somehow requiring an "anticipated explanation" to be usable in the design process. The topic will possibly be addressed in collaboration with ongoing research projects. Ideal candidates will have (or will aim to develop) an interdisciplinary profile.

Contact: Vincenzo D'Andrea vincenzo.dandrea [at]

D1 - Development of methodologies and automatic techniques for the analysis of data acquired by satellite radars in planetary missions (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. 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 data related to the subsurface of celestial bodies that can results in groundbreaking science results (as the recent discovery of water in the subsurface of Mars). 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) and the Sub-surface Radar Sounder on board the EnVision mission to Venus of the European Space Agency (see for more details on the mission).

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at]

D2 / D4 - Development of methodologies based on machine learning and artificial intelligence for the automatic analysis of satellite remote sensing images (1 grant + 1 additional grant)

The automatic analysis of images acquired by Earth Observation satellites is now crucial for many different applications (e.g., precision farming, forestry, analysis of urban areas, monitoring of natural disasters). Even if artificial intelligence and machine learning have been widely used in this context, there are still many methodological challenges and application issues that should be addressed in the analysis 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 images acquired by Earth Observation satellites. The research will be focused on the problems of the semantic segmentation (classification) and information extraction from optical (multispectral and hyperspectral) images acquired by last generation satellites (e.g., PRISMA, Sentinel 2). 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 project activities in progress on the aforementioned topics in the laboratory.

Contact: Lorenzo Bruzzone lorenzo.bruzzone [at]

EURAC Research - Accademia Europea di Bolzano

D5 - Snow cover detection and glacier mass balance estimation with machine learning methods and multi-source satellite data [1 additional grant ]

Changes in glacier area, elevation and mass are major indicators for climate change and are identified as Essential Climate Variables” by the World Meteorological Organization. This PhD project will focus on the development of machine learning methods for snow cover detection over glacierized areas and glacier mass balance estimation, exploiting different sources of satellite data. These will include high and medium resolution multi-spectral images (e.g. Sentinel-2 and Sentinel-3) as well as synthetic aperture radar data (e.g. Sentinel-1). The methodology will be designed and tested over in-situ monitored glaciers in Norway, Svalbard and European Alps. Then, the transferability to glacierized regions with less ground data available will be tested. The project will allow to build and automatically update a consistent time-series of glacier surface mass balance and area change. These are highly valuable data for the hydropower industry, governmental agencies and the research community, e.g. to improve runoff forecast for enhanced water management (e.g. drinking water, hydropower, agriculture) and to increase the knowledge about glaciers as climate variable, their fading in a warming climate and their contribution to global sea-level rise.

Contact: Mattia Cellegari mattia.callegari [at]; Lorenzo Bruzzone lorenzo.bruzzone [at]

Fondazione Bruno Kessler (FBK)

A2 - AI-based 3D inspection for industrial quality control (1 grant)

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

Contact: Fabio Remondino remondino [at]

A3 - Application-oriented Speech Translation (1 grant)

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

Contact: Matteo Negri negri [at] and Marco Turchi turchi [at]

A4 - Computational Models for Human Dynamics (1 grant)

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

Contact: Bruno Lepri lepri [at]

A5 - Domain Adaptive Tiny Machine Learning (1 grant)

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

Contact: Elisa Ricci eliricci [at] - e.ricci [at]

A6 - Neural Models for knowledge driven Natural Language Generation to fight misinformation (1 grant)

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

Contact: Marco Guerini guerini [at]

A7 - Self-configuring resource-aware AI-based speech processing (1 grant)

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

Contact: Alessio Brutti  brutti [at]

A9 - Analysing the effect of counter-narratives on hateful conversations online [1 additional grant ]

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

Contact: Sara Tonelli satonelli [at]

B7 - Adaptive Automated Planning and Scheduling via Combination with Reinforcement Learning (1 grant)

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

Contact: Andrea Micheli amicheli [at]

B8 - AI/ML at the Wireless Network Edge (1 grant)

Data is often collected at the edges of the network but processed centrally fueled by the availability of computing power provided by the cloud. However, the edge of wireless networks can play a role as a distributed platform for ML mitigating the latency and privacy concerns as well as alleviating backhaul network from the transmission of data to the cloud. 
The main goal of this PhD is to investigate the impact of bringing learning at the edges of wireless networks, considering an edge-cloud network which is AI aware and where machine learning algorithms interact with the physical limitations of the wireless medium.

Contact: Cristina Emilia Costa ccosta [at]

B9 - TinyAI for energy-efficient smart sensing in IoT (1 grant)

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

Contact: Elisabetta Farella efarella [at]

C3 - Human-centered AI in the data spaces (1 grant)

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

Contact: Maurizio Napolitano napolitano [at]

C4 - Safety verification and validation of autonomous systems with AI components (1 grant)

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

Contact: Stefano Tonetta tonettas [at]

D3 - Analysis of long and dense remote sensing image time series (1 grant)

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

Contact: Francesca Bovolo bovolo [at]