Awarded Projects

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.

This project will develop the first large-scale foundation models that learn from complete MRI sessions as they occur in hospitals: multiple imaging sequences at their native resolutions, integrated with clinical context such as patient demographics and scanner protocols.

This project develops a scalable foundation model for scientific machine learning on unstructured data, focusing on point cloud representations of physical systems.

This project trains a foundational AI system that enables autonomous agents to trade with each other based on the forecasted value of digital interactions like attention, service events, or decentralized asset flows.

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

This project hopes to propose a new framework centered on point tracking in 3D space to improve spatio-temporal correspondence understanding in video sequences.

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.