Programme Structure

The PhD programme is divided into three years and involves attending advanced-level thematic courses, mostly taught during the first year. Admission to the following year is conditional on passing the exams (at least 90% of the total credits provided for in the educational offer) and active participation in research activities. The second and third years of the programme are dedicated to research activity on a chosen topic and, subsequently, to writing the thesis. During the three years, research seminars are periodically organised, at which each doctoral student presents their work in the presence of the Academic Board. During the programme, PhD students also participate in summer schools and reading groups, with the support of the members of the Academic Board and expert scholars in the field invited by the Department of AI, Data and Decision Sciences, both on a regular basis (as Visiting Professors) and on an ad hoc basis, as well as in research seminars, doctoral workshops and training activities dedicated to soft skills and research skills. Each doctoral student is encouraged to carry out a period of research abroad. In addition, doctoral students have the opportunity to participate in research conferences, including for the purpose of sharing and discussion with the relevant scientific community. The PhD programme is interdisciplinary in nature and covers the following scientific-disciplinary areas: INFO-01/A, IINF-05/A, STAT-01/A, STAT-04/A.

The PhD in Data Science offers an interdisciplinary course that enhances two complementary strengths. On the one hand, the presence of research groups engaged in the theoretical and methodological development of the quantitative foundations of data science – in the fields of computer science, mathematics and statistics – which constitute an autonomous and constantly evolving field of research; on the other hand, the inclusion of these developments in an academic context in which the empirical analysis of economic, institutional and organisational phenomena raises new methodological and theoretical questions, generating a virtuous interaction between formal advancement and data-based application. The integration between these two dimensions therefore makes it possible to offer training closely linked to contemporary international research and, at the same time, to structurally integrate the theoretical and empirical dimensions, promoting the development of original contributions both in terms of methodology and application. A further distinguishing feature of the PhD is the focus on the interaction between data science and artificial intelligence, considered both in its theoretical aspects and in its operational applications. From this perspective, the programme aims to train researchers capable not only of applying advanced predictive tools (e.g. neural networks) but also of analysing their statistical properties, theoretical foundations and decision-making implications. The areas of impact include, by way of example, data-driven support for managerial and strategic decisions; the analysis and prediction of market and organisational behaviour; the evaluation and data-supported design of economic and public policies; the study of complex socio-economic systems through predictive and causal models.

In line with this approach, the programme encourages applications from students with STEM educational backgrounds, even with non-linear trajectories, provided they have a solid quantitative background and robust computational, mathematical and/or statistical skills. Particular attention is given to candidates who have gained research experience or followed interdisciplinary courses, and who demonstrate a strong motivation towards the theoretical, methodological or applied development of data science. The programme encourages applications from groups historically underrepresented in STEM disciplines.