All PhD projects funded under the AI4TheSciences COFUND programme share a common methodological foundation in the development and application of advanced artificial intelligence and machine learning techniques, while intentionally addressing a very broad range of scientific questions. The programme spans domains including physics, cosmology and astrophysics, quantum technologies, advanced materials and energy systems, neuroscience and cognition, medicine and biomedical engineering, biology and ecology, language and cultural studies, as well as economics and social sciences. In the physical sciences, AI is used to model complex systems, integrate observational data with physical constraints, quantify uncertainties, and reveal hidden structures in high-dimensional datasets, contributing to progress in areas such as dark energy studies, space astrometry and materials failure analysis. In engineering-oriented projects, AI supports the design and optimisation of quantum-inspired computing architectures, advanced materials, and energy-related technologies, highlighting the convergence between computation and physical matter.
In the life sciences and medicine, AI-driven approaches are developed for neural signal decoding, brain–computer interfaces, medical image reconstruction, biomechanical modelling, and personalised treatment planning, combining deep learning, physics-informed methods and transfer learning. In parallel, biological and ecological projects employ AI to investigate evolutionary processes, enzyme design, genomic data, to decipher the communication of dolphins, and to monitor biodiversity through environmental DNA, demonstrating the role of AI as a key instrument for modern biological discovery. Language and cognition-oriented projects explore the emergence of linguistic competence in humans and machines, the dynamics of large language models, and the cultural and social dimensions of artificial intelligence. Finally, projects in economics and social sciences apply AI to large-scale behavioural and organisational data to analyse decision-making processes, career dynamics and financial market formation.
Taken together, this portfolio reflects a deliberate strategic choice to position artificial intelligence as a unifying scientific methodology rather than as a standalone discipline. By fostering close interaction between methodological innovation and domain-specific applications, the AI4TheSciences PhD programme contributes to shaping a new paradigm of interdisciplinary, data-driven scientific research in Europe.
