Awarded Projects

Drug cardiotoxicity is a major health issue due to the need to develop new safe and efficient drugs in a fast manner.

The urgent need for decarbonisation of the steel manufacturing sector has made hydrogen-based technologies an important prospective element of the envisioned low-carbon energy systems.

HyperCast AI addresses this challenge by developing and benchmarking state-of-the-art deep learning architectures to downscale global weather and subseasonal-to-seasonal (S2S) predictions to unprecedented 1km hourly resolution.

Atomic nuclei constitute more than 99% of visible matter. Thus, an accurate description of nuclear systems is central to our understanding of the universe.

This project focuses on investigating the fundamental aspects of hydrogen turbulent combustion.

This proposal will contribute to building two European Storm-Resolving Earth System Models (SR-ESMs), applying them to study the Earth system and answering important open questions about how its climate will change over the next decades.

The primary challenge is to obtain an accurate impact rate for the last 1 Gyr of evolution due to substantial particle loss. We are further challenged by the duration of the simulations versus that of the project.

Climate change and pollution urge the aeronautical sector to reduce its environmental footprint.

This project addresses some limitations in he primary workhorses of quantum simulations by developing thermaMLS2, a real-space machine-learned correlation model that builds on our recent work on local-energy learning and perturbation-theory-based density functionals.