Computer Science
Doctoral programme (PhD)
A.Y. 2025/2026
Study area
Science and Technology
PhD Coordinator
The doctoral programme in Computer Science aims to provide students with advanced scientific, methodological and technological knowledge in computer science and related sectors and their corresponding fields of application. This knowledge will prepare students and introduce them to theoretical and applied research, with particular attention to interdisciplinarity and internationalisation, developing research skills so that they are able to produce original independent research of interest to the international scientific community and businesses.
The doctoral programme aims to provide students with:
- solid wide-ranging knowledge on the basics of science and methodologies and technologies pertinent to IT and related fields,
- advanced and in-depth skills in specific areas,
- interdisciplinary skills necessary to promote cultural and methodological synergies,
- sound knowledge of research methodologies and of how to organise and manage research and disseminate results,
- opportunities to train internationally,
- a better preparation and placement within academic research groups and companies.
The doctoral programme aims to provide students with:
- solid wide-ranging knowledge on the basics of science and methodologies and technologies pertinent to IT and related fields,
- advanced and in-depth skills in specific areas,
- interdisciplinary skills necessary to promote cultural and methodological synergies,
- sound knowledge of research methodologies and of how to organise and manage research and disseminate results,
- opportunities to train internationally,
- a better preparation and placement within academic research groups and companies.
Tutte le classi di laurea magistrale - All classes of master's degree
Dipartimento di Informatica "Giovanni degli Antoni" - Via Celoria, 18 - Milano
- Main offices
Dipartimento di Informatica "Giovanni degli Antoni" - Via Celoria, 18 - Milano - Degree course coordinator: Roberto Sassi
[email protected] - Degree course website
http://www.di.unimi.it/ecm/home/didattica/dottorato/
| Title | Professor(s) |
|---|---|
| IoT-edge systems certification
Requirements: Knowledge of the main certification and assurance techniques. Knowledge of cloud-edge architectures and systems |
|
| Non-functional verifications on LLM-Based Systems
Requirements: Knowledge of the main verification, monitoring, and non-functional testing techniques. Understanding of LLM models and adaptation methodologies such as fine-tuning, prompting, and RAG |
|
| Enhanced Techniques for AI-driven Data Governance and Trust in Big Data Pipelines
Requirements: Knowledge of the main big data and Artificial Intelligence architectures and technologies. Knowledge of the main data governance solutions for big data architectures. Knowledge of Trust Management Systems and Protocols. |
|
| Certification of Distributed Systems built on Machine Learning/Artificial Intelligence
Requirements: Knowledge of the main machine learning/artificial intelligence tecniques. Knowledge of the main assurance/certification techniques. |
|
| Robust Machine Learning Techniques for the Classification of Archaeological and Artistic Heritage Artifacts: Addressing Domain-Specific Challenges in the Context of Cultural Heritage
Requirements: Experience in Computer Vision and Machine Learning. Proficiency in Python programming and practical experience with Deep Learning libraries. |
|
| Techniques for procedual synthesis and spatial rendering of sound
Requirements: Digital signal processing; Audio programming |
|
| Development and evaluation of accessible music interfaces
Requirements: Audio and MIDI programming; experimental methods in HCI |
|
| Learning and Planning Techniques for Autonomous Robots and Agents
Requirements: Basics of algorithms and machine learning |
|
| Explainable AI techniques for human activity recognition in smart environments | |
| Geometric centralities: discriminating power and expressivity
Requirements: - |
|
| Innovative approaches on applied games for clinical treatment of children with disabilities based on integration of emotional intelligence, smart objects, social robots, game design and machine learning. | |
| Automatic generation of storytelling for video games based on playstiles and emotional reactions of players
Requirements: good skills in: game design and development AI for videogame HCI, decision systems, LMM |
|
| Learning Interpretable Representations of Hierarchical Structures with Hyperbolic and Quantum-Inspired Embeddings for Biomedical Data Integration and prediction
Requirements: mathematical basis, graph theory and graph analysis, supervised learning |
|
| Graph Representation Learning for Biomedical applications
Requirements: Basics of mathematics, graph theory and analysis, supervised learning |
|
| Universal Language Server Protocol and Debugger Adapter Protocol for Modular Language Workbenches | |
| Language Feature Modularization, Composition and Polyglotism in Compiled Languages as Rust | |
| Integrating Large Language Models in Process Mining for Decision Support
Requirements: Python and NLP frameworks |
|
| Regret minimization algorithms in strategic environments
Requirements: Theoretical foundations of machine learning. Fundamentals of game theory. Algorithm design and analysis |
|
| Multi-agent sequential decision-making
Requirements: Theoretical foundations of machine learning. Design and analysis of algorithms. |
|
| Data driven mathematical programming: integrating mathematical programming, machine learning, and probabilistic methods
Requirements: Background on mathematical programming, statistics, machine learning, design and experimental analysis of algorithms |
|
| Secure computation techniques and blockchain applications | |
| Privacy preserving machine learning and applications | |
| Circuit design for emerging technologies and quantum computing | |
| Logic synthesis for enabling information security | |
| Discrete optimization algorithms for industrial applications
Requirements: Algorithms and Data Structures, Operations Research, C programming |
|
| Algorithms for Combinatorial Optimization problems applied to complex decisions
Requirements: Algorithms and Data Structures, Operations Research, C programming |
|
| Trustworthiness of deep discriminative, predictive, and generative AI models. | |
| Efficient distillation of large-scale AI models for resource-constrained environments. | |
| Data security and privacy in emerging scenarios | |
| Security and privacy in biometric systems | |
| Less-constrained biometric recognition systems | |
| Investigating biases and ethical issues in NLP and visual transformers
Requirements: NLP and machine learning |
|
| Proof theory and decision procedures for Justification Logics
Requirements: Knowledge of the basic notions of semantics and proof theory for classical logic. |
|
| Security-aware resource management in cloud-fog scenarios | |
| Succinct machine leaning models
Requirements: Basic knowledge of machine learning and optimization. |
|
| Learned data structures
Requirements: Basic knowledge of machine learning. |
|
| Distributed architectures for entertainment applications
Requirements: networking, distributed systems |
|
| Interconnection, security, and privacy issues for portable personal-use devices
Requirements: networking, distributed systems, security |
|
| Unsupervised learning in artificial intelligence: learning from unlabeled data | |
| Less-constrained monitoring in Industry 4.0 by signal/image processing, artificial intelligence, and machine learning | |
| Hypercomplex Learning for Computer Vision
Requirements: Computer Vision, Geometric Deep Learning |
|
| Development and Optimization of Fuzzing Techniques for Automated Vulnerability Discovery in Software Systems
Requirements: - Advanced knowledge of cybersecurity, particularly software vulnerabilities and exploitation techniques. - Proficiency in low-level programming languages, such as C and C++, with a solid understanding of memory management. - Experience with dynamic analysis and fuzzing tools, such as AFL, LibFuzzer, or QEMU-based frameworks. |
|
| Advanced Techniques for Embedded System Security: Analysis, Defense, and Resilience Against Hardware and Software Threats
Requirements: - Solid understanding of embedded architectures (e.g., ARM, RISC-V) and real-time operating systems (RTOS). - Experience in low-level programming and firmware development, including direct hardware interfacing. - Knowledge of hardware and software security concepts, including side-channel attacks, secure bootloaders, and protection of sensitive data. |
|
| User empowerment in regulating private information release and assessing misinformation in online scenarios
Requirements: Basic knowledge of data protection (e.g., anonymization, privacy metrics, access control) and/or basic NLP algorithms and explainability mechanisms. |
|
| Informatics in K-12 education in Italy: design, analysis, and empirical validation of teaching methods and materials
Requirements: programming, basics in computer science education |
|
| Computational Models for the Analysis and Representation of Musical Information: Musicological Approaches and Digital Technologies
Requirements: Foundations of music theory (basic knowledge of harmony, rhythm, notation, and musical structures); Elements of musicology (familiarity with traditional analytical methods and basic notions of music history); Skills in music computing (experience with software tools for music analysis and processing); Programming (basic knowledge of programming languages, preferably with applications in the musical domain); Knowledge of formats and standards for music representation (MIDI, MusicXML, MEI, IEEE 1599, or other structured formats). |
|
| Digital Technologies for Music Education
Requirements: Fundamentals of music theory; fundamentals of music pedagogy; skills in music informatics; programming; knowledge of formats and standards for music representation. |
|
| Text-to-Spatial SQL interfaces | |
| Machine learning in emerging scenarios
Requirements: Prerequisites: Statistics, machine learning, computer programming |
|
| Artificial intelligence methods for anomaly detection
Requirements: Prerequisites: Statistics, machine learning, computer programming |
|
| Data management and artificial intelligence in medicine | |
| Assistive technologies | |
| Formal Languages and Classical and Quantum Automata
Requirements: Theoretical Computer Science, Formal Languages and Automata |
|
| Application of LLM techniques for knowledge extraction from bio-medical documentation
Requirements: Basic knowledge of machine learning and knowledge management |
|
| Plausibility assessment for link prediction on bio-medical knowledge graph
Requirements: Basic skills of graph representation learning and knowledge management |
|
| Learning to program: identifying difficulties and how to overcome them, studying also the relationship with current code generation tools
Requirements: Programming, Computer Science Education |
|
| Human-AI Collaborative Writing
Requirements: Knowledge on Natural Language Processing (NLP) |
|
| Explainable deep learning for audio signal processing (bioacoustics and medical acoustics)
Requirements: Machine learning, Signal processing, Statistics |
|
| Formal Systems and Complexity
Requirements: Automata and formal languages |
|
| Intelligent systems for industrial and environmental applications based on IoT architectures and artificial intelligence | |
| Artificial Intelligence solutions for dependable and sustainable cloud-fog-edge computing | |
| New paradigms in sound synthesis and manipulation
Requirements: Depending on the projects proposed, preference will be given to profiles that focus more on mathematical signal processing, programming of real-time audio algorithms or interactive system design. |
|
| Computational Creativity and Artificial Intelligence for Sound and Music
Requirements: Mathematical skills and basic knowledge of artificial intelligence are required. Knowledge of signal processing, programming, acoustics and musical skills are also welcome. |
|
| Orchestration and optimization of service in 5G/6G mobile networks
Requirements: Wireless and Mobile Networks, Machine Learning, Edge Computing, Operative Research, Simulation |
|
| Applied formal methods in Digital Twins
Requirements: Skills in Formal Methods and Temporal Logic |
|
| Formal methods for Security- and Safety-critical Systems
Requirements: Skills in Formal Methods and Temporal Logic, Skills in security and safety |
|
| Optiimization algorithms for autonomous agents coordination
Requirements: Operational research, Mathematical programming, Algorithms |
|
| Optimization on graphs
Requirements: Operational research, Mathematical programming, Algorithms |
|
| Personality and emotion models for interactive agents in VR environments
Requirements: good skills in: - design and development of VR application for gaming - C++ - Machine Learning - Human-computer -interaction |
|
| Design and realization of smart objects for rehabilitation | |
| Design and development of artificial intelligence algorithms for the analysis of electrocardiograms
Requirements: Knowledge of signal processing and/or artificial intelligence are suggested |
|
| Computational models for the technical analysis of human factors and color rendering in lighting systems
Requirements: colorimetry |
|
| Methods of analysis and computational formalization of board games | |
| Quantum modeling and computing for sound, music, and multisensory interaction
Requirements: Elements of linear algebra and audio signal processing |
|
| Definition of sensory spaces and control of trajectories for interaction design
Requirements: Elements of human-computer interaction, signal processing, and statistical learning |
|
| Data privacy and artificial intelligence | |
| Biomedical signal processing for a patient-centric digital health | |
| Multimodal learning for context-aware and transferable understanding in AI models | |
| Deep learning: learning techniques and explainability | |
| Geometry processing: processing of 3D surfaces and volumes for real-time rendering, physics simulation, digital fabrication, animation, and design support | |
| Architectures and algorithms for high-performance and energy-efficient genomic pattern matching | |
| Development of efficient algorithms for large-scale phylogenetic reconstruction from genomic data | |
| Efficient and Explainable Foundation Models for Pathogenic Variant Ranking in Rare Diseases via Multi-Modal Genomic Integration
Requirements: Basics of Mathematics, basics of supervised learning and LLMstransformers |
|
| Large Language Models for Precision Medicine
Requirements: basics of mathematics, supervised learning, basics of LLMs |
|
| High-speed cryptography
Requirements: Background knowledge: cryptography and algebra |
|
| Cryptanalysis of cryptographic primitives
Requirements: Background knowledge: cryptography and algebra |
Enrolment
Places available: 8
Call for applications
Please refer to the call for admission test dates and contents, and how to register.
Application for admission: from 15/05/2025 to 13/06/2025
Application for matriculation: from 14/07/2025 to 18/07/2025
Attachments and documents
Following the programme of study
Contacts
Office and services for PhD students and companies