Awarded Projects

Galaxies and the gas surrounding them are turbulent and multiphase, i.e., colder (< 10^4 K) gas is embedded in a much larger, volume filling hot (≳ 10^6 K) phase, and regulates the fuel supply for star formation and black hole growth.

This project aims to leverage EuroHPC JU compute resources to advance AI-assisted debugging and self-correcting software systems through large-scale training and benchmarking of neural architectures for reverse execution and dynamic slicing.

This project aims to revolutionise the field of tabular data science, leading the foundation model revolution in this trillion dollar market.

This project leverages cutting-edge generative AI models to design synthetic enzymes optimized for industrial applications in biofuels, agriculture, and pharmaceuticals.

This project will develop a general reinforcement-learning framework that can fine-tune protein-generative models towards any computable design objective.

This project addresses the critical gap between the promise of general-purpose AI (GPAI) models in medical imaging and their reliable clinical implementation through a two-phase approach combining systematic robustness evaluation and targeted domain adaptation.

It is widely acknowledged that the prediction of turbulent flows in the presence of separation is one of the most significant challenges in fluid dynamics.

Although current Earth system models (ESMs) project a consistent pattern of future global warming, there are important regional differences that increase the uncertainty at the local scale, which poses a risk for climate adaptation.

This project combines large-scale generation of safe and deliberately unsafe robot demonstrations with frontier vision-language models to produce fine-grained, temporally consistent safety explanations over video sequences