The PhD programme is aimed at Master’s graduates with a solid basic quantitative background and has the following objectives:<\/p>\n
The programme aims to integrate the most advanced AI methodologies with the theoretical pillars of quantitative disciplines. The educational path ensures that the acquisition of high-level computational skills is always supported by the formal rigour that is essential for producing sustainable and reproducible scientific innovation.<\/p>\n
The first year will focus on advanced content that reinforces and complements the skills already possessed in the fields of computer science, mathematics and statistics, with a clear aim of preparing students for research. The educational pathway includes two types of teaching, common to all subject areas: Core courses and Frontiers courses. The Core courses provide conceptual and methodological tools through the study of optimisation, stochastic processes, statistical inference, high-dimensional modelling and the design of efficient algorithms, enabling students to acquire the theoretical and computational tools necessary to contribute to research in Data Science. The Frontiers courses, on the other hand, focus on the most recent lines of research and on frontier topics in the various fields of interest, offering students direct contact with emerging developments and approaches in the scientific community. The second and third years of the programme are dedicated to research activity on a chosen topic and, subsequently, to writing the thesis.<\/p>\n
| DATA SCIENCE XLII CICLO<\/th>\n<\/tr>\n | ||||
|---|---|---|---|---|
| 1\u00b0 YEAR 2026\/2027<\/th>\n<\/tr>\n | ||||
| Term 1 – September \/ December 2026<\/th>\n<\/tr>\n | ||||
| Activity<\/th>\n | Subject<\/th>\n | Professor<\/th>\n | hs<\/th>\n | CFU<\/th>\n<\/tr>\n |
| COURSES<\/th>\n | Machine Learning on Evolving Data<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n |
| Graph Mining and Network Intelligence<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n | |
| Analysis of Multi-Agent Systems<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n | |
| Static and Dynamic Optimization for Data Science and Machine Learning<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n | |
| Bayesian Learning for Complex Data and Dynamic Systems<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n | |
| Functional Data Analysis<\/td>\n | TBD<\/td>\n | 16<\/td>\n | 4<\/td>\n<\/tr>\n | |
| MASTERCLASS<\/th>\n | Frontiers in Computer Science and Artificial Intelligence<\/td>\n | TBD<\/td>\n | 8<\/td>\n | 2<\/td>\n<\/tr>\n |
| Frontiers in Mathematical Sciences<\/td>\n | TBD<\/td>\n | 8<\/td>\n | 2<\/td>\n<\/tr>\n | |
| Frontiers in Statistical Sciences<\/td>\n | TBD<\/td>\n | 8<\/td>\n | 2<\/td>\n<\/tr>\n | |
| <\/td>\n | 30<\/td>\n<\/tr>\n | |||
| Term 2 – February \/ May 2027<\/th>\n<\/tr>\n | ||||
| Activity<\/th>\n | Subject<\/th>\n | Professor<\/th>\n | Hs<\/th>\n | CFU<\/th>\n<\/tr>\n |
| OTHER ACTIVITIES<\/th>\n | Reading groups<\/td>\n | TBD<\/td>\n | 60<\/td>\n | 15<\/td>\n<\/tr>\n |
| Department and Early-career research seminars<\/td>\n | TBD<\/td>\n | 20<\/td>\n | 5<\/td>\n<\/tr>\n | |
| Summer school<\/td>\n | TBD<\/td>\n | 40<\/td>\n | 10<\/td>\n<\/tr>\n | |
| <\/td>\n | 30<\/td>\n<\/tr>\n | |||
| 2\u00b0 YEAR 2027\/2028<\/th>\n<\/tr>\n | ||||
| Term 1 – September \/ December 2027<\/th>\n<\/tr>\n | ||||
| Activity<\/th>\n | Subject<\/th>\n | Professor<\/th>\n | hs<\/th>\n | CFU<\/th>\n<\/tr>\n |
| COURSES<\/th>\n | Academic career, ethics and integrity in research (joint with PhD in Management)<\/td>\n | Zattoni<\/td>\n | 6<\/td>\n | 1<\/td>\n<\/tr>\n |
| Meta-research skills for data scientists<\/td>\n | TBD<\/td>\n | 8<\/td>\n | 2<\/td>\n<\/tr>\n | |
| OTHER ACTIVITIES<\/th>\n | Reading groups<\/td>\n | 32<\/td>\n | 8<\/td>\n<\/tr>\n | |
| Department seminars<\/td>\n | 8<\/td>\n | 2<\/td>\n<\/tr>\n | ||
| Research activities<\/td>\n | 64<\/td>\n | 16<\/td>\n<\/tr>\n | ||
| <\/td>\n | 29<\/td>\n<\/tr>\n | |||
| Term 2 – February \/ May 2027<\/th>\n<\/tr>\n | ||||
| OTHER ACTIVITIES<\/th>\n | Department seminars<\/td>\n | 2<\/td>\n<\/tr>\n | ||
| Research activities & thesis writing<\/td>\n | 25<\/td>\n<\/tr>\n | |||
| Presentation at PhD conference<\/td>\n | 4<\/td>\n<\/tr>\n | |||
| <\/td>\n | 31<\/td>\n<\/tr>\n | |||
| 3\u00b0 YEAR 2028\/2029<\/th>\n<\/tr>\n | ||||
| OTHER ACTIVITIES<\/th>\n | Research activities & thesis writing<\/td>\n | 25<\/td>\n<\/tr>\n | ||
| Final thesis preparation<\/td>\n | 35<\/td>\n<\/tr>\n | |||
| Totale CFU<\/th>\n | 180<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":" The PhD programme is aimed at Master’s graduates with a solid basic quantitative background and has the following objectives: To provide advanced training on the methodologies underlying data science, including the main techniques of Machine Learning and Artificial Intelligence and combining theoretical rigour, mastery of modelling and analysis tools, and attention to the development and […]<\/p>\n","protected":false},"author":23,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"categories":[],"tags":[],"yoast_head":"\n |