The PhD program will adopt a strongly interdisciplinary perspective, preparing graduates to address complex problems through the integrated use of mathematics, physics, high-performance computing, and statistics across a broad range of domains, including chemistry, medicine, and the social sciences.
To support this goal, several partner companies, healthcare institutions, and humanities or social science research centers will provide rich and challenging datasets for analysis.

Through this exposure, students will learn to apply established methodologies and to develop innovative techniques aimed at answering complex questions that arise in diverse fields such as bioinformatics, neuroscience, economics, the social sciences, medicine, law, and the humanities.

The mission of the program is to equip graduates with cutting-edge tools to address current and future challenges in data analysis, whether involving large-scale or small-scale datasets. Students completing the program will be prepared to approach data-driven research problems with innovative and creative solutions, generating new knowledge through studies that demonstrate strong intellectual merit and the potential for broader societal impact.

Data mining, machine learning, statistical analysis, and computational simulation will constitute key components of the students’ daily training. Equally essential will be developing the ability to foster effective collaboration between technical experts and end-users of quantitative models—a fundamental element of the program’s interdisciplinary vision.

The PhD program will actively promote collaborations with leading international experts. Distinguished scientists will contribute to the teaching activities and advanced training modules. Students employed by one of the companies supporting the program will undertake a research project defined in agreement with the company and jointly supervised by an academic advisor and an industrial mentor. Each student’s research is expected to result in publications in high-quality international scientific journals or in the development of patentable innovations.

The research themes of the program are primarily aligned with the PE1, PE2, and PE6 classes of the European Research Council (ERC) scientific domains. However, the range of application areas is considerably broader and encompasses numerous additional ERC categories, including SH1, SH2, SH3, SH4, SH6, PE4, LS2, LS9, among others. Furthermore, Data Science is formally recognized as a strategic research area within the HORIZON 2020 framework.