Reserved topic scholarships

Department of Information Engineering and Computer Science

A1 - Efficient Context Management for Multimodal Conversational (Project: IARPA - Video Lincs – Rota; Prime Contract Nr. 56000026C0025 - Subcontract Nr. PO130479; CUP E63C26000510005); (Project: ROTA-CT PAT 2026; CUP F81F21000010006)  (1 grant)

The research activity will focus on efficient memory mechanisms for long-horizon video understanding, with particular emphasis on tracking and re-identification in multimodal conversational agents. The research will investigate methods to compress, update and retrieve visual-temporal histories, enabling models to reason over identities, trajectories, occlusions, viewpoint changes and long temporal gaps without storing or reprocessing the entire visual context. The candidate will develop novel techniques for adaptive visual memory, compressed track histories, identity-aware multimodal reasoning and privacy-preserving interaction with visually grounded agents. A preferred background including sociological studies for human behavior
understanding is preferred, together with empirical validation on real-world video-understanding tasks such as multi-object tracking, person/object re-identification, temporal and causal reasoning, and video question answering.

Contact: Paolo Rota paolo.rota@unitn.it

A2 - Neurosymbolic Reinforcerment Learning in Dialogue (1 grant)

Neurosymbolic Reinforcement Learning in Dialogue, combines neural learning with symbolic reasoning to develop adaptive, interpretable, and robust dialogue systems. While neural models are effective at handling language variability, they often lack transparency, consistency, and efficient long-term decision-making. Reinforcement learning offers a framework for optimizing dialogue strategies through interaction, but benefits from symbolic knowledge such as rules, constraints, dialogue states, and task structures. The project will investigate how symbolic representations can guide exploration, improve sample efficiency, and constrain learned policies, while neural models manage uncertainty and the complexity of natural language. This work will contribute toward explainable, goal-oriented conversational agents.

Contact: Giuseppe Riccardi giuseppe.riccardi@unitn.it

A3 - Novel Riemannian Frameworks for Representation Learning in Non-Euclidean Spaces (Project: GUIDANCE (Debugging Computer Vision Models via Controlled Cross- modal Generation) FIS2023-03251; CUP E53C25000420001) (1 grant)

This project aims to develop novel Riemannian geometric frameworks for machine learning through three closely connected directions: 1) the research will construct and analyze new Riemannian structures capable of representing complex relationships beyond Euclidean flat geometry and hyperbolic geometries primarily designed for hierarchical data; 2) the project will develop new principles and methods for building neural network architectures on different manifolds, enabling geometric representation learning in non-Euclidean spaces; 3) the research will investigate the practical impact of these frameworks across a broad range of machine learning tasks, including the use of Riemannian manifolds as representation spaces for foundation models and large-scale neural architectures, as well as latent spaces for generative models. By providing geometrically grounded representations, the project aims to improve the expressiveness, efficiency, and interpretability of next-generation foundation models operating on complex and structured data.. This research is supported by the project GUIDANCE (Debugging Computer Vision Models via Controlled Cross-modal Generation) FIS2023-03251 – CUP E53C25000420001.

Contact: Niculae Sebe niculae.sebe@unitn.it

A4 - Toward Adaptive Multimodal Conversational Agents (Project: IARPA - Video Lincs – Rota; Prime Contract Nr. 56000026C0025 - Subcontract Nr. PO130479; CUP E63C26000510005); (Project: UE HE ELLIOT - RICCI; GA: 101214398 – ELLIOT; CUP: E63C25001010006); (Project: ROTA-CT PAT 2026; CUP F81F21000010006) (1 grant)

The research activity will focus on personalized multimodal dialogue, with particular emphasis on efficient adaption of video-grounded conversational agents to individual users through human feedback. The research will investigate methods to compress and retrieve long dialogue histories, tailor responses to user preferences, and interpret the model's visual reasoning. The candidate will develop novel techniques for adaptive memory, human-aligned fine-tuning, and privacy-preserving interaction
with visually grounded agents. The project will combine probabilistic and statistical modelling, for which a strong mathematical and statistical background is expected, with empirical validation on real-world video-understanding tasks such as temporal and
causal reasoning and video question answering. This research is partially supported by the project ELLIOT (European Large Open Multi-Modal Foundation Models For Robust Generalization On Arbitrary Data Streams) – CUP E63C25001010006.

Contact: Paolo Rota paolo.rota@unitn.it Massimiliano Mancini massimiliano.mancini@unitn.it

​​​​​​B2 - AI-based XR systems for real-time interaction in the Musical Metaverse (Project: UE HE MUSMET – TURCHET; CUP E63C24002360006) (1 grant)

The candidate will work on the design and implementation of MR platforms supporting real-time multi-user musical interactions, with a particular focus on Mixed Reality scenarios where musicians will perform with real instruments and audiences engage simultaneously across physical and virtual spaces. A key area of exploration will be the use of generative AI for 3D content generation to enable dynamic environments and avatars generation within networked immersive platforms.

Contact: Luca Turchet luca.turchet@unitn.it

​​​​​​B3 - AI4SecWallets - using AI for experimental vulnerability detection, repair, and validation for EU Digital Identity Wallet applications (1 grant)

The project will go through three phases: (1) develop vulnerability detection approaches for EU Digital Identity Wallet (EUDI) implementations by taking into consideration the EUDI requirements and regulations and may combine machine learning techniques with traditional techniques like static or dynamic code analysis to detect potential security issues in the wallet implementations; (2) extend automated vulnerability repair techniques for EUID wallet-specific vulnerabilities by suggesting automated corrections or exploits for the EUID wallet-specific vulnerabilities detected during the security analysis process; (3) experimentally evaluate the proposed vulnerability detection, repair, and exploit techniques on a EUID wallet implementation.

Contact: Fabio Massacci fabio.massacci@unitn.it

​​​​​​B4 - Collaborative learning and planning algorithms for human-robot teams (Project: UE-HE-LS-HARMONICA; GA 101294331; CUP E63C26000780006) (1 grant)

The PhD aims to develop intelligent systems that help people, robots, and digital tools collaborate more effectively in recycling processes. The system should be able to observe the work environment, understand the condition and needs of operators, learn from their skills, and distribute tasks in the most appropriate way between people and robots. The final goal is to improve safety, efficiency, operator well-being, and the acceptance of robotic technologies in industrial settings.

Contact: Edoardo Lamon edoardo.lamon@unitn.it Matteo Saveriano matteo.saveriano@unitn.it

​​​​​​B5 - Electromagnetic Modeling and Digital Twin Development for Plant-Based Communication Systems (Project: UE - HE – ECOSENTINEL; GA n. 101186925; CUP E63C24001890006) (1 grant)

Part of the European EcoSentinel project, this research focuses on modeling and simulating electromagnetic signal propagation for wireless systems using plants as natural antennas. The candidate will also develop a Plant Digital Twin, implementing machine learning algorithms to infer plant health and soil conditions from environmental and transmission metrics.

Contact: Lorenzo Lizzi leonardo.lizzi@unitn.it

​​​​​​B6 -Stakeholders Analysis of Security for AI and AI for Security and its Policy Implications (Project: HORIZON EUROPE -Sec4AI4Sec Massacci; G.A. 101120393; CUP E63C23000720006) (1 grant)

The candidate will develop empirical, ethnographic studies (from field observation to focus group and interviews up to to randomized experiments) on how different stakeholders perceive threats and benefits in the combined use of AI and cybersecurity (in particular for software vulnerabilities, offense and defense cyber capabilities, and threat intelligence). The studies including students, developers, security experts, and company stakeholders will form the scientific grounds for drafting recommendations for both European Commission policies and innovation and technology transfer actions. The focus of policy papers will be to provide insight to policy makers on the regulation of the digital transition and how information asymmetry may impact power structures in the usage of security and AI in which corporations may take roles traditionally assigned to national states. This activity will also be in cooperation with the phd students in the Collaborative Doctoral Program with the JRC on horizontal cybersecurity

Contact: Fabio Massacci fabio.massacci@unitn.it

C1 - Towards a sociotechnical model of the Cyber-creative Process

The 2025 World Economic Forum report identifies creative thinking as a key economic driver alongside Generative AI (GenAI). While current GenAI demonstrates creative capabilities comparable to the average human performance, it lacks intentionality, relies on human prompting, and was trained on biased data. Research on Human–AI collaboration has separated the creative product from the process. While AI products may appear creative, human well-being depends on active engagement in the creative process, which is embedded in the sociotechnical context where products are produced. Action is needed to ensure that AI enhances rather than mortifies human creativity. The PhD project will study the cybercreative process using user-centred and participatory design approaches, grounded in cognitive, social, and societal determinants. Higher education will be used as the research field, comparing and contrasting how different disciplines (computer science, design and the social sciences) are integrating GenAI and co-designing new interaction protocols with students and instructors.

Contact: Vincenzo D'Andrea vincenzo.dandrea@unitn.it

D1 - Artificial intelligence and data processing for planetary radars  (Project: ASI JUICE RIME 2021 Bruzzone; CUP F65F21000950005. Project: ASI EnVision fase B1 Bruzzone; CUP F63C22000650005) (1 grant)

The research is related to data processing and machine learning for the analysis of data acquired by planetary radar sounders. Radar sounders operate from satellite platforms and acquire data related to the subsurface of planetary bodies that can results in groundbreaking science results. 
The PhD will be developed in the framework of radars on board of two planetary missions of European Space Agency, i.e., the Sub-surface Radar Sounder (SRS) on board the EnVision mission to Venus of the European Space Agency (ESA) (for more information refer to https://www.esa.int/Science_Exploration/Space_Science/Envision) and the Radar for Icy Moon Exploration (RIME) on board of the JUpiter ICy moons Explorer (JUICE) of ESA (see https://www.esa.int/Science_Exploration/Space_Science/Juice).
The activity will be focused on the development of methodologies for the processing and the automatic analysis of the above-mentioned data for information extraction (e.g., classification, semantic segmentation, data inversion). Special emphasis will be given to methodologies that exploit the most recent developments in the framework of deep learning.
Research will be developed at the Remote Sensing Laboratory (https://rslab.disi.unitn.it/)

Contact: Lorenzo Bruzzone lorenzo.bruzzone@unitn.it

Fondazione Bruno Kessler (FBK)

Intellectual Property Notice for PhD candidates under the UniTrento-FBK Agreement 
Please read the following information carefully before submitting your application. 
Intellectual Property of Research Results.The intellectual property rights of research results generated by PhD students under scholarships within the UniTrento-FBK Agreement shall belong to FBK.
Transfer of Intellectual Property Rights. FBK will establish agreements with PhD students regarding the transfer of intellectual property rights related to their research results.
Collaboration with UniTrento. If UniTrento academic staff contribute to research results obtained through PhD scholarships funded by FBK, the determination of IP shares will be defined through separate written agreements based on each party’s contribution. PhD students are required to collaborate with UniTrento in all necessary activities related to the joint management of IP.

A5 - Integrating Values, Beliefs, and Social Dynamics into Conversational Agents  (1 grant)

Providing neural models with a persona profile has demonstrated that conditioning such models on specific characteristics produces more coherent and engaging conversations. However, current representations remain shallow, typically relying on static biographical facts. In reality, human communication is shaped by both profound internal traits - such as goals, opinions, values, and beliefs - and dynamic situational factors, including emotional states, conversational context, and the specific relationship between speakers. The goal of this PhD Thesis is to bridge these dimensions, moving beyond rigid profiles to develop adaptive, context-sensitive persona models. By integrating deep profile characteristics with real-time social dynamics, the research aims to advance the state of language generation for conversational agents across multiple domains and languages.

Contact: Marco Guerini guerini@fbk.eu

A6 - Knowledge-Driven Natural Language Generation for Combating Hateful Misinformation  (1 grant)

The convergence of online misinformation and hate speech poses a significant threat to social cohesion, especially when manipulated content is used to incite hostility toward marginalized groups. While large language models (LLMs) offer potential for fact-checking and counterspeech, current systems often struggle with factual accuracy, complex reasoning, and the nuanced social dynamics of harmful content. This PhD project aims to overcome these hurdles by developing knowledge-enhanced neural generation frameworks. The research will integrate external knowledge and computational argumentation to produce reliable, persuasive, and ethically grounded responses, while also addressing the moral values and motivations of users spreading hate-based misinformation. Ultimately, this work seeks to advance generative AI that can mitigate harmful discourse and clarify its  underlying mechanisms.

Contact: Marco Guerini guerini@fbk.eu

A7 - Reasoning in Multimodal Models (1 grant)

The student is expected to work on novel methods for reasoning in large multimodal models, with a focus on enabling multimodal systems to understand, interpret, and reason over visual and textual information jointly. Research topics may include compositional reasoning, grounded inference, multimodal chain-of-thought methods, evaluation benchmarks, and trustworthy AI. The project aims to advance the capabilities of next-generation AI and robotics systems for complex real-world tasks requiring robust multimodal understanding and reasoning.

Contact: Elisa Ricci e.ricci@unitn.it

A8 - Structured Semantic Enhancement of 3D Scene Representations (1 grant)

Neural reconstruction pipelines (e.g. Gaussian Splatting) and 3D sensing technologies such as LiDAR and photogrammetry now enable the digitisation of spatial environments across urban, natural and indoor settings. However, raw 3D observations generally remain largely unstructured and difficult to directly exploit for downstream applications such as mapping, simulation, analyses and other related uses.
This PhD investigates methods to transform raw 3D data into structured scene representations that integrate geometry, object-level organization and semantic information. The goal is to produce enriched 3D representations that can be interpreted and reused as meaningful digital assets.
Therefore, the goals of the proposed PhD are:
(i) To investigate novel neural scene representation methods and their complementary with respect to conventional geometric methods
(ii) To develop methods to decompose raw 3D scenes into consistent object-level components and associate them with semantic categories and attributes
(iii) To design mechanisms to refine incomplete or ambiguous scene regions into coherent structures
(iv) To study how semantic information can propagate across spatially connected regions
(v) To extend robustness across heterogeneous environments (outdoor, indoor, etc.)
The successful candidate is expected to have a strong background in artificial intelligence and 3D computer vision, with solid programming skills and an interest in 3D data processing (e.g. point clouds and neural scene representations), along with familiarity with foundation models (e.g. vision language models) and the ability to design and prototype innovative, reliable and reproducible solutions for complex 3D scene understanding tasks.

Contact Fabio Remondino remondino@fbk.eu

A9 - Human-centred Evaluation Frameworks for Multilingual Technologies (1 grant)

How we measure and define performance shapes the systems we build. Current Natural Language Processing (NLP) benchmarks often prioritize leaderboard scores over practical utility, failing to capture how models behave in real-world, socially situated contexts. This PhD project treats evaluation methodology as a research problem in its own right, advancing both the conceptual foundations and computational tools for assessing NLP systems in ways that are reliable, valid, and human-centred. Application domains include multilingual settings, such as machine translation, and emerging agentic and interactive multimodal NLP systems involving human-AI collaboration present frontier evaluation challenges. The ultimate goal is to develop evaluation frameworks that capture not only overall system performance, but also real-world utility, fairness, and responsiveness to the needs of diverse stakeholders.

Contact: Matteo Negri negri@fbk.eu

B7 - Artificial Intelligence for Tiny, Connected Devices: Enabling Learning and Inference on Resource-Limited Networked Embedded Systems (1 grant)

The Internet of Things (IoT) paradigm is driving a massive increase in the generation of multimodal data on tiny, resource-constrained devices at the far edge of the computing infrastructure. Given the challenges of limited computation, energy constraints, and communication bottlenecks, there is a growing need to process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices. Still, many challenges are ahead. Expanding their impact in real-world deployments requires addressing the heterogeneity of hardware, data, and resource availability in distributed scenarios. This research will explore novel approaches for enabling AI at the edge, focusing on one or all of the following aspects: i) hardware-aware scaling, model compression, and novel approaches for diverse low-power edge devices in distributed and collaborative IoT scenarios; ii) strategies for efficient and adaptive learning on-device or across a network of heterogeneous nodes while minimizing energy consumption and bandwidth usage; iii) investigating how explainability and robustness can be maintained in compressed models deployed at the far edge, ensuring trustworthiness and reliability in real-world applications This interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low-power Hardware and protocols will be tailored to the candidate's profile and interests, contributing to the development of innovative solutions for real-world challenges.

Contact: Elisabetta Farella efarella@fbk.eu

C2 - Failure Propagation Analysis for Safety Assessment of Complex Systems (1 grant)

The design process of complex systems must guarantee not only the functional correctness of the implemented system, but also its safety, dependability, and resilience with respect to run-time faults. To this aim, complex systems implement mechanisms to timely detect components' faults and to isolate them, before they can propagate and cause system failures. Hence, the design process must characterize the likelihood and severity of faults, identify the set of possible hazards and failure conditions, mitigate possible consequences, and assess the effectiveness of the adopted mitigation measures.  
Model-Based Safety Analysis (MBSA) is listed as an acceptable and recommended means of compliance to perform safety assessment in the latest issue of SAE ARP4761A, specifically for analyzing failure propagation. MBSA is based on the adoption of a formal, mathematical model of the system and on a tool-supported methodology to assist the generation of safety artifacts. State-of-the-art tools for MBSA implement functionalities to generate Minimal Cut Sets (MCS) from a fault propagation model and a Top-Level Event (TLE) [IMBSA25, LPNMR22, CAV21]; perform automated fault injection into a behavioral design model to generate the corresponding safety model [FAOC21, TACAS16]; generate Minimal Cut Sets from a fully behavioral dynamical model and a TLE [FAOC21, TACAS16, CAV15a, SCP15]; perform various kind of validation of fault propagation models against behavioral models [IJCAI16, AAAI16, AAAI15]. 
The objective of this study is to advance the state-of-the-art in failure propagation analysis and safety assessment of complex systems. In particular, it will investigate extensions of existing formalisms to deal with aspects such as the timing of fault propagation, the characterization of transient and sporadic faults, and the analysis of the effectiveness of fault mitigation measures in presence of complex fault patterns. Moreover, this study will investigate the use of fault propagation models for the design of fault detection, isolation and recovery (FDIR) components. To this aim, fault propagation models will be extended with observability information and used to solve problems such as anomaly detection, diagnosis, root-cause analysis, and prognosis. Finally, this study will aim to bridge the gap between fault propagation models and fully behavioral system models used for the design and safety assessment of complex systems.

Contact: Marco Bozzano bozzano@fbk.eu

C3 - Symbolic Model Checking techniques for embedded systems (1 grant)

Techniques based on formal methods for the verification and validation of embedded and safety-critical 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 remains a challenge in practice, due to factors such as poor scalability, lack of automation, the interplay between computation and physical aspects, or 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 systems, with particular emphasis on safety-critical application domains such as railways, automotive, avionics, and aerospace. A particular attention will be devoted to improving the scalability and degree of
automation of formal methods techniques, with a specific focus on symbolic model checking methods using satisfiability and satisfiability modulo theories solvers as symbolic reasoning engines. 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@fbk.eu

C4 - Modeling and Simulation of Urban Digital Twins (1 grant)

An Urban Digital Twin is a dynamic digital model of a city, fed by data collected from the city itself, and capable of faithfully reproducing the city behaviour through the use of advanced modelling, data science, and AI techniques. One of the key foreseen applications of Urban Digital Twins is to predict the evolution of the city, including the effects and impacts of external changes (eg, climate change) and internal processes (eg, urban transition policies and incentives).
A challenge for the development of Urban Digital Twins is that cities are systems-of-systems, with complex interactions between physical, organisational, and social dimensions, as well as between the physical world and the digital world. Novel modeling and simulation techniques are necessary to develop Urban Digital Twins able to manage this complexity and produce reliable predictions. 
The candidate will be requested to contribute to this research area, and in particular to work on advanced modeling and simulation frameworks for Urban Digital Twins. The expected contribution of the candidate is twofold: first, developing a novel framework of new analytical methods for modeling and simulation in the Urban Digital Twin; second, assessing the validation of the proposed framework on real-world scenarios concerning the adoption of Digital Twin by Italian cities.
Besides the requirements established by the rules of the IECS school, preferential characteristics for candidates for this scholarship are:
- Master degree in Computer/Data Science, Mathematics, Physics, Electrical Engineering, Communication Engineering, or equivalents;
- Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation.

Contact: Marco Pistore pistore@fbk.eu

Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)

B8 - Advanced Machine Learning Methods for Climate Science (1 grant)

The PhD project will focus on the development of advanced machine learning methodologies for the analysis and modeling of complex climate systems. The research will address the efficient exploration of computationally expensive parametric spaces, the construction of surrogate models and scalable computational systems for accelerating and enhancing numerical simulations, and the quantification of uncertainty associated with predictions and model outputs. Particular attention will be given to the integration of observational data, physical models, and machine learning techniques, promoting the development of reliable, interpretable, and explainable approaches and systems capable of providing transparent insights into decision-making processes and supporting next-generation climate science applications.

Contact: Sandro Luigi Fiore sandro.fiore@unitn.it Paola Nassisi paola.nassisi@cmcc.it

National Cybersecurity Agency

B1 - Real-Time-Aware Trusted Execution Environments for Hardware-Rooted Identity and Mission Execution Integrity (CUP E66E26000040001) (1 grant)

Following the approval of the final ranking list related to the Call for Funding of PhD Scholarships in the field of cybersecurity by the National Cybersecurity Agency (NCA), prot. no. 215430, dated 19.06.2026, and the successful submission of the project proposal by the University of Trento (Project ID XLII_123953_118_UniTN), a scholarship funded by the NCA is being offered for the development of the  research project: B1 - Real-Time-Aware Trusted Execution Environments for Hardware-Rooted Identity and Mission Execution Integrity (CUP E66E26000040001)

The project's characteristics, timeline, objectives, and expected outcomes are clearly outlined in the research project's description (pdf file).

In accordance with the Implementing Regulations of the NCA Call for Proposals, the PhD student who won the scholarship must commit to the following:
•    carry out the doctoral studies in accordance with the research project that has been accepted for funding (see research project description - pdf file);
•    sign the statement of intent, completed using the template provided by the Agency;
•    spend a period of mobility abroad;
•    submit an annual report on the research activities carried out, according to the template prepared by the NCA, including any publications in progress or completed, etc;
Since the submission of the application, the applicant is aware of the following:
•    the use of the NCA name, brand, or any other distinguishing feature for advertising or promotional purposes is prohibited, with the exception of instances where the doctoral scholarship's funding is acknowledged;
•    without prejudice to the moral rights of authorship held by the doctoral students, the University and the Agency shall each hold 50% ownership of the research results generated by the NCA-funded project.;
•    The scholarship will be revoked in the following cases:
     a) implementation of the doctoral studies that diverge from the original project which was accepted for funding;
     b) termination of the Doctoral Programme, negative outcome of the annual assessment for the renewal of the scholarship, withdrawal from the scholarship, award of the degree;
•    the University retains the right to recover from the beneficiary any amounts received.

Contact: Bruno Crispo bruno.crispo@unitn.it

Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS) - Department of Information Engineering and Computer Science

B9 - Novel AI-Based Approaches and Tools for Modeling Water Cooling Systems in Exascale Data Centers (Program HPC-TRES - https://www.ogs.it/it/high-performance-computing-laboratory-hpc-tres): (Project: UE HE LS ENSURE Fiore; GA n. 101292847; CUP E63C26001040006) (1 grant)

The topic focuses on the development and application of novel Artificial Intelligence (AI) methods and software tools for modelling water cooling systems in exascale data centers. Contributions are expected to explore how AI techniques, including machine learning, deep learning, surrogate modelling, and physics-informed approaches, can complement or accelerate high-fidelity thermo-fluid simulations to support the design, monitoring, optimization, and control of cooling infrastructures. The topic links to digital twins, predictive maintenance, and energy-aware operation for next-generation HPC facilities.

Contact: Sandro Luigi Fiore sandro.fiore@unitn.it Stefano Salon ssalon@ogs.it