Awarded Projects

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.

Machine learning plays a pivotal role in extending the reach of quantum-chemistry methods for simulating molecules and materials.

The goal of this project is to improve the linguistic proficiency of the public LLM while continuing to offer skills that are similar to newer closed models, and thus compete with a new service in the LLM market for Basque.

This project proposes novel methods leveraging multi-modality and foundation models to correct SpT data for unwanted sources of variation while preserving the biological signal and at adapting downstream analyses including adapting differential gene expression analysis (DGE) to newly corrected data.