Computer Science
Doctoral programme (PhD)
A.Y. 2026/2027
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) |
|---|---|
| Formal systems and complexity | |
| Deep learning for audio pattern recognition
Requirements: machine learning, statistics, signal processing |
|
| Quantum modeling and computing for sound, music, and multisensory interaction
Requirements: Elements of linear algebra and audio signal processing |
|
| Learning under Uncertainty, Drift, and Hallucination
Requirements: Machine learning |
|
| Innovative approaches on applied games for clinical treatment of children with disabilities based on integration of emotional intelligence, game design and machine learning. | |
| Data security and privacy in emerging scenarios | |
| Less-constrained biometric recognition systems | |
| Artificial intelligence and machine learning for image processing in biomedical applications | |
| Dependable and sustainable Cloud/Fog/Edge Computing: artificial intelligence for resource and task allocation for performance, energy consumption, fault tolerance, and resilience | |
| Efficient distillation of large-scale AI models for resource-constrained environments. | |
| Deep learning: learning techniques and explainability | |
| Controlled management of private information and 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. |
|
| Centrality measure distributtions
Requirements: Network centrality; Belief propagation |
|
| Controlled and collaborative query execution in emerging scenarios | |
| Data privacy and artificial intelligence | |
| Artificial Intelligence techniques in mobile and pervasive computing applications
Requirements: Solid knowledge of CS topics including programming and ML |
|
| Geometric Inductive Biases for Physiological Sensing
Requirements: Computer Vision, Computational Geometry |
|
| Foundation Models for Process Mining: Process Representation, Discovery, and Prediction
Requirements: Basic knowledge of machine learning and deep learning techniques. Familiarity with large language models (LLMs) and transformer-based architectures. Foundational understanding of Business Process Management (BPM) and process representation formalisms (e.g. Petri nets, BPMN). Experience with data analysis programming languages, particularly Python. |
|
| Artificial Intelligence techniques for behavioral monitoring in smart home environments
Requirements: Machine learning, Data Analysis, Distributed Systems |
|
| Design and development of artificial intelligence algorithms for the analysis of cardiac signals
Requirements: Knowledge of signal processing and/or artificial intelligence are suggested |
|
| Perceptual analysis and inclusive design methodologies of the chromatic and visual aspects of analog, digital and mixed game components | |
| Computational Models for the Analysis and Representation of Musical Information: Musicological Approaches and Digital Technologies
Requirements: Fundamentals of music theory (including basic notions of harmony, rhythm, notation, and musical structure); Foundations of musicology (including familiarity with traditional analytical approaches and basic knowledge of music history); Basic competencies in music computing (including the use of software tools for music analysis and processing); Basic programming skills (including familiarity with at least one programming language, preferably applied to the musical domain); Knowledge of music representation formats and standards (e.g., MIDI, MusicXML, MEI, IEEE 1599, or other structured formats). |
|
| Algorithms for Combinatorial Optimization problems applied to complex decisions
Requirements: Algorithms and Data Structures, Operations Research, C programming |
|
| Security Analysis of Cryptographic Primitives with and without Artificial Intelligence
Requirements: cryptography |
|
| Diffusion-Based Generative Modeling for Uncertainty-Aware 3D Hand Pose and Mesh Estimation
Requirements: CV and AI: Probabilistic pose estimation in video, Multi-hypothesis prediction, SOTA research direction in diffusion models |
|
| Innovative techniques for procedual synthesis and spatial rendering of sound
Requirements: Digital signal processing; Audio programming |
|
| Language Feature Modularization, Composition and Polyglotism in Compiled Languages as Rust
Requirements: development and design of programming languages |
|
| Artificial Intelligence Techniques for Continuous and Adaptive Compliance of Distributed Systems
Requirements: Knowledge of the main artificial intelligence techniques. Knowledge of the main assurance and compliance techniques. |
|
| Explainable Temporal Graph Neural Network Framework for Dynamic patients' Risk Stratification and Intervention
Requirements: supervised/unsupervised machine learning; graph theory |
|
| 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 |
|
| Active perception and efficient planning for autonomous agricultural robots.
Requirements: Basics of algorithms, optimization, and machine learning. |
|
| Artificial Intelligence methods for molecular modeling and precision medicine
Requirements: Basic machine learning and artificial intelligence background |
|
| Unsupervised Reinforcement Learning | |
| Distributed architectures for entertainment applications
Requirements: networking, distributed systems |
|
| Algorithmic Foundations of Machine Learning
Requirements: Fundamentals of Machine Learning, Calculus, Linear Algebra, Probability and Statistics, Algorithms. |
|
| Design of succinct neural network-based algorithms for supervised and semi-supervised learning
Requirements: Basic knowledge of machine learning and optimization |
|
| Data management and artificial intelligence in medicine | |
| Assistive technologies | |
| Certification of Cloud Edge Continuum systems
Requirements: Knowledge of the main certification and assurance techniques. Knowledge of cloud-edge architectures and systems. |
M. Anisetti
|
| Deep learning methods for natural language processing
Requirements: Basic knowledge of mathematics (linear algebra and probability) and machine learning principles. Basic programming skills (preferably in Python) and familiarity with fundamental NLP concepts. |
|
| Learning to program: identifying difficulties and how to overcome them, studying also the relationship with current code generation tools
Requirements: Programming, Computer Science Education |
|
| Non-Functional Assessment of LLM-Based Applications
Requirements: Prerequisites: Knowledge of the main non-functional assessment, monitoring, and testing techniques. Knowledge of LLM models and adaptation techniques such as fine-tuning, prompting, and RAG. Basic knowledge of Agentic AI architectures. |
|
| Algorithms for mining, sampling, counting in large graphs and hypergraphs
Requirements: Graph theory, algorithms and complexity |
|
| AI-Driven Fuzzing and Software Protection through Autonomous Agents for Automated Vulnerability Discovery and Mitigation
Requirements: Basic knowledge of C/C++ programming, operating systems, and software security (e.g., buffer overflows, UAF). Familiarity with fuzzing and introductory concepts in AI/ML and Python scripting. |
|
| AI-Driven Visual Analysis of Cultural Heritage Artifacts and Imagery
Requirements: Experience in Computer Vision and Machine Learning. Proficiency in Python programming and practical experience with Deep Learning frameworks. |
|
| Application of Agentic AI in Biomedical Data Curation
Requirements: Machine learning, data management, knowledge graph |
|
| Quantum-Safe Secure Multi-Party Computation
Requirements: advanced cryptographic knowledge |
|
| Biomedical signal processing for a patient-centric digital health | |
| Analysis of algorithms for sequential decision-making
Requirements: Fundamentals of Machine Learning, Calculus, Linear Algebra, Probability and Statistics, Algorithms. |
Enrolment
Places available: 7
Call for applications
Please refer to the call for admission test dates and contents, and how to register.
Application for admission: from 11/05/2026 to 10/06/2026
Application for enrolment: from 06/07/2026 to 10/07/2026
Attachments and documents
Following the programme of study
Contacts
Office and services for PhD students and companies