Awarded Projects

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.

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.

Skeleton-based forensic human identification strongly relies on manual, error-prone methods that can benefit from data-driven automated software alternatives. For this project, experts in generative AI for forensics will contribute to development of a Craniofacial Reconstruction tool.

This project proposes a novel, fully automated dual-loop computational workflow for inverse materials discovery that couples a generative AI model for crystal structure creation with a mixed machine-learned interatomic potential (MLIP) / ab initio (DFT) high-throughput active-learning labeling loop.

Protein design has shifted in the last 2 years by the development of novel generative models for protein design.