• CURRICULUM: Industry, Transportation, and Natural Sciences

1. Foundations of machine learning and artificial intelligence. 
2. Neuro-symbolic computing and explainable artificial intelligence
3. Reinforcement Learning and control for Cyber-Physical Systems and industry 4.0
4. Machine learning and statistical inference in natural sciencesHPC methods and algorithms for simulation and (big-) data analysis in physics
5. Computer vision and control for smart manufacturing, industry 4.0 and natural sciences
6. Mathematical, heuristic and evolutionary optimisation and applications to smart cities and smart transportation

7. Big data management and curation and HPC-based artificial intelligence

    

• CURRICULUM: Medicine, Life Sciences, and Environment

1. Causal Inference methods from Observational Data in epidemiological research.
2. Machine Learning for Healthcare: interpretability, explainability and transparency issues.
3. Deriving Biomedical Knowledge from EHR (Electronic Health Records)  
4. Artificial Intelligence and Computer Vision for estimating biodiversity indexes: challenges and opportunities

5. Aggregation of biodiversity data: standouts and protocols

    

• CURRICULUM: Economy and Society

1. Statistical and computational methods in social sciences
2. Statistical and computational methods in economics and finance
3. Artificial intelligence in government and its potential applications from a public policy perspective
4. Artificial intelligence and social media
5. Artificial intelligence for disaster response
6. Network analysis: methods and applications
7. Public engagement activities and their impact on participants' attitudes towards artificial intelligence

The ADSAI PhD Programme combines basic research in data science and artificial intelligence with their applications both in scientific and industrial contexts. The Academic Board includes researchers in fundamental disciplines such as computer science, mathematics, and statistics, as well as researchers from applied areas of interest including physics, engineering, social sciences, and biology, from both academic institutions and companies. The PhD Programme is structured into three curricula focusing on the main application of the areas of interest: Industry 4.0, Medicine and Life Sciences, and Society and Economics. The Programme's applied nature is further realized through agreements for scholarships with a specific research topic signed with institutions and companies, which collaborate with PhD students in terms of both training and research project development. These agreements and collaborations also allow to exploit synergies with research institutions within the region as well as with the industrial sector.

The PhD Programme aims to train researchers with a thorough scientific preparation in the methodological foundations (informatics, mathematics, statistics) of data science and artificial intelligence, as well as their applications in various domains. The reference application areas include medicine and life sciences, industry 4.0, society and economics, data-driven science, with particular attention to their impact on the territory and society. The training in the first year includes education on the fundamentals and applications of data science and AI, including some ethical and legal aspects, as well as courses on organizational aspects of research and analysis of the state of the art. PhD students will be made aware of the principles of research reproducibility and FAIR data. The education will be personalized based on the student's previous study plan and the research topic pursued by the doctoral candidate. In the second and third years, scientific activity will be developed, potentially including a period of stay at internationally relevant research institutions. Common aspects of the training include a multidisciplinary theoretical-experimental approach, which is a qualifying aspect of both research and interaction with the territory and the productive world. PhD students will be given the opportunity to develop their communication skills: specific courses will be offered and their participation in educational and dissemination activities will be encouraged, both in scientific conferences/workshops and in public events. The main training goal is to prepare professionals in theoretical and applied research at an excellent level on the international stage.

ADSAI PhD Graduates can pursue an academic path in research and teaching in the disciplines of the PhD Programme (statistics, computer science, mathematics). More generally, they can contribute with the tools of data science and artificial intelligence in research areas where they can be relevant. Their attention to practical aspects, also in close connection with public and private bodies operating in these areas, will enable PhD graduates to leverage the tools of data science and artificial intelligence in operational contexts within public or private organizations: companies, research institutions, public administrations. The close connection with numerous local institutions and companies is likely to promote the integration of PhD graduates into operational environments, both through numerous themed scholarships co-financed by companies and institutions and through their collaboration on educational projects.