The ADSAI PhD program

“Artificial Intelligence is the new electricity.” Andrew Ng, Co-founder and lead of Google Brain.

The Applied Data Science and Artificial Intelligence (ADSAI) PhD program at the Univeristy of Trieste started with the 2021 cohort, and since 2021 ADSAI is also part of the Italian National PhD program in Artificial Intelligence.

Motivation and objectives

Why a PhD in applied Data Science and Artificial Intelligence?

The aim of the new ADSAI PhD programme is to train students to master the modern tools of Data Science and Artificial Intelligence, preparing them to create new analysis methods while giving them the expertise to apply cutting-edge tools to problems in the real world. Our research spans from theoretical to applied Machine Learning, by understanding how modern methodologies became popular we frame their current limitations and highlight their possibility of extension.

What do students achieve with the ADSAI Phd program?

ADSAI students will master modern concepts of machine learning and statistical analysis, corroborated by strong analytical problem-solving skills. For instance, graduates will be trained in deep learning and neural networks, in Bayesian methods and sampling algorithms for probabilistica graphical models, in probabilistic programming for scalable inference, high performance computing and software development.

Upon graduation, students will be proficient in delivering a complete Data Science solution to a complex real-world problem from beginning to end. Training in core disciplines will be complemented with the possibility to attend modules focusing on ethical aspects of data analysis, and the impact of technological development on regulations and society.

What is the marketplace of ADSAI students?

ADSAI graduates will be attractive for top-tier Data Science and innovation companies, and will have scientific profiles ready to enter the academic world, in Italy and abroad. Given the broad impact of Data Science and Artificial Intelligence in modern society, industry and business, graduates will have the opportunity for a fulfilling career in a variety of fields. Potential careers paths range from the financial sector to digital services, healthcare, technology companies, market research and many others.

Program structure and curricula

ADSAI is a 3-years PhD programme structured in two overlapping parts and three curricula:

  • [Part 1] in the first part (approx. 12 months), students spend most time training to consolidate theoretical and applied methodologies from the broad area of Data Science and Artificial Intelligence. This constitutes core knowledge-base to succesfully implement a PhD project in the second part of the programme, and serves to level differences across PhD students with different backgrounds;

  • [Part 2] in the second part (approx. 24 months)s, students undertake their PhD research work that culminates with a final PhD thesis at the end of the third year. Training in the last two years is intended to be more advanced and focused to the specific research area of the student.

Each student enrolls in one of three curricula, with the opportunity to pursue both pure and industry-related research questions:


The ADSAI faculty board is composed of a highly cross-disciplinary group of experts from the University of Trieste, which tightly interact with colleagues at the following institutions:

On occasions, external members include other researchers from the broader Trieste area.

What type of mentorship should a student expect?

Upon enrollment, ADSAI students identify at least one supervisor from the ADSAI faculty board. On occasion, some projects might involve the opportunity of a research placement in industry or in another research institute. In those cases, one or more external supervisors might also be available.

The supervisors will mentor the student to define the most effective training program during the whole ADSAI PhD program, taking into account the student’s background and objectives. The student will identify his/ her research question in tight collaboration with the supervisors. All students will be encouraged and supported to present their work at international conferences and meetings at the appropriate point in time of their studies.