The PhD program emphasizes interdisciplinary research, connecting theory and practice across data science, artificial intelligence, and their applications in medicine, industry 4.0, and social sciences. 

In the "Industry, Transportation, and Natural Sciences" curriculum, we address key research questions in machine learning and AI while applying them to diverse scenarios. Research focuses on explainable AI, geometric machine learning, control theory, optimization, Bayesian statistics, and reinforcement learning. These methods are applied to smart manufacturing, renewable energies, robotics, geophysics, cosmology, and material science. 

In the "Medicine, Life Sciences, and Environment" curriculum, we utilize AI to analyze genomic data, medical images, electronic health records, and epidemiological data in fields such as oncology, neurological disorders, and infectious diseases. We also explore biodiversity informatics, providing insights to adapt and mitigate global environmental changes. 

The "Economy and Society" curriculum focuses on developing data integration techniques, statistical learning approaches (Bayesian, semi-parametric, and non-parametric), and network analysis algorithms. Applications target social and economic phenomena, focusing on fairness, bias detection, and social networks.