Awarded Projects

The project aims to develop openly accessible world models to foster transparency and collaboration in the field of self-driven cars, addressing key challenges such as data curation and model scalability.

The project aims to develop an advanced foundation model for high-resolution geospatial analysis, with a specific focus on imagery from European regions.

This project combines the state-of-the-art data mining technique called Active Learning with the recently developed FeNNol library for training Machine-Learning-based force fields.

The goal of this project is to reach a new frontier in precision radiology through novel deep learning techniques applied at unprecedented scale.

As energy-efficient, and renewable energy carrier, hydrogen plays an important role in the energy transition for reducing greenhouse gas emissions and limit climate changes. However, on the Earth hydrogen is not freely available, it is bound in molecules from which it should be extracted.

The computational grant will enable researchers to access massive HPC resources to speed up the assessment of the combustion performance of the proposed configurations by evaluating the flame stabilization mechanism and pollutant emissions.

Astrophysical plasma turbulence has been studied extensively over the past decades. Due to the weak collisionality of this plasma, turbulence plays a fundamental role in the process of heating and accelerating the solar wind: driving energy fluctuations towards smaller and smaller scales.

Information storage based on phase-change materials (PCM) is widely considered a promising alternative to flash memories for the non-volatile memory technologies of the next decade.

This proposal aims to address the challenges of data scarcity for domain-specific fine-tuning of Large Language Models (LLMs) in languages other than English.