Awarded Projects

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 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.

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 project proposes a new, challenging benchmark for generated content detection that contains highly realistic images thanks to the use of image conditioning and of very effective and recent models.