Computer Science
Doctoral programme (PhD)
A.Y. 2023/2024
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) |
|---|---|
| Non-functional Intent-driven monitoring in Cloud Edge Continuum
Requirements: Knowledge of principal solutions for monitoring and testing of non functional properties. Knowledge of Edge and Cloud Architectures |
|
| Certification and risk assessment of Machine Learning/Artificial Intelligence models.
Requirements: Knowledge of the main machine learning/artificial intelligence tecniques. Knowledge of the main assurance and risk management techniques. |
|
| Sound synthesis and 3D rendering for Virtual and Augmented Realities
Requirements: Knowledge in digital signal processing; Audio programming |
|
| Development and evaluation of accessible music interfaces
Requirements: Knowledge of audio and MIDI programming; experimental methods in HCI |
|
| Explainable AI techniques for human activity recognition in smart environments | |
| Behavior anomaly detection in smarthome with applications to digital health | |
| AI-based methods for autonomous planning with mobile robots and agents.
Requirements: Basics of algorithms, optimization, and machine learning |
|
| Centrailities and symmetries in hypergraphs | |
| E-Health: integration of domotics, service robots, exer-games, virtual comunities and web services through intelligent systems and emotional intelligence to support pre-frail people at home. | |
| Development of deep neural networks based on convolutional layers for reinforcement learning: extraction of state-action patterns from applications in different domains. | |
| Innovative approaches on applied games for clinical treatment of children with disabilities based on integration of emotional intelligence, game design and machine learning. | |
| Modeling and learning from long-term cyclic environmental dynamics for autonomous mobile service robots using ML and AI. | |
| Confidential Computing | |
| AI methods for biological, clinical, and multi-omics data integration
Requirements: Strong mathematical knowledge, machine learning |
|
| Universal Language Server Protocol and Debugger Adapter Protocol for Modular Language Workbenches
Requirements: Good skill in problem solving and programming |
|
| Graph-based Process Mining
Requirements: Basic notions of Process Mining and Programming |
|
| Design and analysis of adaptive algorithms for online decision-making
Requirements: Foundations of machine learning. Design and analysis of algorithms. |
|
| Design and analysis of algorithms for active and semi-supervised learning
Requirements: Foundations of machine learning. Design and analysis of algorithms. |
|
| Large Scale Prescriptive Analytics: solving optimization problems in real contexts, where large scale, dynamic and heterogeneous data make classical techniques unsuitable
Requirements: Mathematical modeling, operations research, statistics, simulation, design and experimental analysis of algorithms |
|
| Modeling of security properties and applications for Distributed ledgers | |
| Privacy preserving malware detection and federated learning | |
| Approximate logic synthesis and applications to emerging technologies | |
| Logic synthesis for quantum circuits | |
| Algorithms for Combinatorial Optimization problems applied to complex decisions
Requirements: Knowledge of algorithms and Data Structures, Operations Research, C programming |
|
| Discrete optimization algorithms for industrial applications
Requirements: Knowledge of algorithms and Data Structures, Operations Research, C programming |
|
| Smart and dynamic service orchestration in modern networks
Requirements: Knowledge of the main techniques for containerized service deployment. Knowledge of cloud-edge infrastructure and services and 5G. |
|
| Mobility data science | |
| Data security and privacy in emerging scenarios | |
| Less-constrained biometric recognition systems | |
| Security and privacy in biometric systems | |
| Deep learning methods for extracting knowledge from unstructured data sources.
Requirements: Good understanding of machine learning fundamentals, NLP and deep learning, Python programming |
|
| Multimodal language models for language pragmatics, interpretability and causal inference.
Requirements: Good understanding of machine learning fundamentals, NLP and deep learning, Python programming |
|
| Controlled and collaborative query execution in distributed systems | |
| Semi-supervised learning based on parametric Hopfield networks for unbalanced data classification | |
| Graphics-oriented hybrid cloud architecture
Requirements: Skills in cloud computing, graphics |
|
| 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. | |
| Computational Intelligence and applications
Requirements: Skills in Probabilistic Modeling and Data Analytics. Basic knowledge of Machine Learning. Fluency in Python. |
G. Gianini
|
| Multimodal signals in affective computing and machine perception
Requirements: Skills in signal processing, machine learning, affective computing |
|
| Computer vision and learning models for human understanding
Requirements: Knowledge of Computer Vision, Artificial Intelligence |
|
| Malware Analysis | |
| Software Protection | |
| The impact of GPT-based code generators on programming education | |
| Computer-Based Technologies for Music Education
Requirements: Basic knowledge in Computer Science (programming languages, databases, etc.) and Music (music theory, fundamentals of harmony) |
|
| Digital Assistive Technologies for Music
Requirements: Basic knowledge in Computer Science (programming languages, databases, etc.) and Music (music theory, fundamentals of harmony) |
|
| Streaming of interactive 3D virtual environments
Requirements: Skills in networking, graphics, virtual reality |
|
| Distributed architectures for entertainment applications
Requirements: Skills in networking, distributed systems |
|
| Data driven mathematical programming: integrating mathematical programming, machine learning, and probabilistic methods.
Requirements: Knowledge of Mathematical programming, statistics, machine learning, design and experimental analysis of algorithms |
|
| Models and methods for learning succinct data structures | |
| Models and methods for learning fuzzy sets.
Requirements: Knowledge of Machine learning, statistics. |
|
| Assistive technologies on mobile devices | |
| Data management and artificial intelligence in medicine | |
| Construction and Analysis of Knowledge Graphs for Biomedical Applications
Requirements: Knowledge of graph-based data management systems, good knowledge of Machine learning techniques, good programming skills in python |
|
| Verification and validation of programming language theory
Requirements: Knowledge of Logic, functional programming |
|
| Identify and overcome the main difficulties in learning to program | |
| Data Science for Computational Social Sciences and Humanities
Requirements: Solid background in computer science, with particular focus on machine learning and data management. |
|
| Deep learning for audio and music signal processing
Requirements: Advanced statistics, machine learning, python |
|
| Dependable and sustainable Cloud/Fog/Edge Computing: artificial intelligence for resource and task allocation for performance, energy consumption, fault tolerance, and resilience | |
| Intelligent systems for industrial and environmental applications based on IoT architectures and artificial intelligence | |
| Formal methods for Security- and Safety-critical Systems
Requirements: Skills in Formal Methods and Temporal Logics. Skills in security and safety |
|
| Applied formal methods in Digital Twins
Requirements: Skills in Formal Methods and Temporal Logics |
|
| Combinatorial optimization algorithms
Requirements: Operations reseearch, algorithms and data-structures |
|
| Unobtrusive dynamic evaluation of the emotional impact induced by the contents of interactive virtual environments that should evolve in real time in order to adapt to each specific user.
Requirements: Good knowledge of: game design and programming, virtual reality and artificial intellingence for video games |
|
| Design of graph-based artificial intelligence algorithms for the analysis of biomedical signals and images
Requirements: Knowledges of artificial intelligence and graph theory are suggested. |
|
| An advanced model of color deficiencies | |
| Energy-efficient secure and private distributed data management and processing | |
| Biomedical signal processing for a patient-centric digital health | |
| Design of signal processing algorithms to extract digital biomarkers from electrocardiograms
Requirements: Knowledge of signal processing and artificial intelligence suggested. |
|
| Ambient intelligence: data analysis and machine learning for self-adaptive environments | |
| Deep learning: learning techniques and explainability | |
| Scalable data structures for high resolution, detailed 3D models
Requirements: Esperiences in Geometry processing, or computer graphics. Programming skills preferred. |
|
| Artificial Intelligence for Healthcare
Requirements: Knowledge of Machine Learning and Deep Learning are welcome |
|
| Algorithm for evolutionary biological processes
Requirements: Programming and algorithm analysis |
|
| AI algorithms applied to data analysis in Intensive Care Unit
Requirements: Knowledge in machine learning and deep learning |
|
| Deep Learning for Genomic Medicine | |
| The security of crypto building blocks
Requirements: Background knowledge: cryptography and algebra |
|
| High-speed cryptography
Requirements: Background knowledge: cryptography and algebra |
|
| Machine learning on graph for blockchain networks
Requirements: Basic knowledge on machine learning on graphs and blockchain-based technologies |
|
| Development of computational models for the construction and analysis of oncological KGs (ex DM 117/2023)
Requirements: biomedical data analysis, data fusion algorithms, basics of immunology |
|
| Development of systems for the automatic recognition of anthropometric facial landmarks in telemedicine (ex DM 117/2023)
Requirements: Knowledge and practice in using models for machine learning (deep learning), computer vision |
|
| Development of learning models based on neural networks for the study of human behavior and cognitive processes (ex DM 117/2023)
Requirements: Good knowledge of mathematics and statistics, knowledge and practice in the use of models for machine learning (deep learning), computer vision. |
|
| AI-driven Knowledge Modeling for the Digital Transformation in Humanities and Social Sciences (ex DM 118/2023)
Requirements: Solid background in computer science, with particular focus on machine learning and data management. |
|
| Assessing the Effectiveness of Dynamic and Static Analysis Methods in Detecting Advanced Malware, Exploiting Emerging HW Technologies
Requirements: Understanding fundamental cybersecurity principles, technical programming skills, and the ability to work effectively in team |
|
| IT methods and technologies for transparency in the Public Administration (ex DM 118/2023)
Requirements: Coding and teamwork skills |
Courses list
December 2023
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Advanced Topics in Signal Processing | 2 | 10 | English | |
| Data Visualization | 2 | 10 | English | |
January 2024
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Matheuristics for Combinatorial Optimization Problems (Module 1) | 2 | 10 | Italian | |
| Methods for Statistical Model Fitting | 2 | 10 | English | |
| Sequential Decision-Making with Applications to Digital Markets | 2 | 10 | English | |
February 2024
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Artificial Intelligence for Network Medicine | 4 | 20 | English | |
| Deep Learning in Bioinformatics | 4 | 21 | English | |
| Leveraging Machine Learning in Process Mining | Gianini Gabriele
|
2 | 10 | English |
| Matheuristics for Combinatorial Optimization Problems (Module 2) | 2 | 10 | English | |
March 2024
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Advanced Artificial Intelligence Models and Methods | 2 | 10 | English | |
| Data Warehouse Architectures and Technologies: Solutions and Still Open Issues | 4 | 16 | English | |
| Network Design (modeling, analysis and optimization of networks part 2) | 2 | 10 | English | |
June 2024
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Autonomous Mobile Robotics and Embodied Agents | 2 | 10 | English | |
| Data Security and Privacy in Emerging Scenarios | 2 | 10 | English | |
| Efficacy and Efficiency Evaluation of Machine Learning Models | 3 | 15 | English | |
July 2024
| Courses or activities | Professor(s) | ECTS | Total hours | Language |
|---|---|---|---|---|
| Optional | ||||
| Resources Allocation in Mobile Edge Computing | 2 | 10 | English | |
Enrolment
Places available: 14
Call for applications
Please refer to the call for admission test dates and contents, and how to register.
Session: 1
Application for admission: from 06/04/2023 to 05/05/2023
Application for matriculation: from 06/06/2023 to 12/06/2023
Attachments and documents
Qualifications assessment criteria
Session: 2
Application for admission: from 27/06/2023 to 26/07/2023
Application for matriculation: from 25/09/2023 to 07/10/2023
Attachments and documents
Following the programme of study
Contacts
Office and services for PhD students and companies