Awarded Projects

Leveraging high quality internal data of the European Institutions at scale to build an EU institutional large language model (LLM)

This proposal centers on investigating two primary research questions:

(i) the influence of Reynolds number on rough-wall turbulence, emphasizing inner/outer region interactions, and

(ii) the effects of wall curvature and its interplay with pressure gradients.

Computer-aided drug design can significantly reduce the time and resources needed for drug discovery because experimental high-throughput assays can be replaced with virtual screens.

CAD files are widely used in manufacturing sectors such as automotive, aerospace, and construction. Traditionally, these CAD files are archived as images, making it difficult to retrieve structured information.

The project aims to obtain a new state-of-the-art in both ML parameterisation and stochastic parameterisation, with the potential to significantly reduce and better characterize model uncertainty in both weather and climate models.

This project leverages advanced Machine Learning techniques, specifically Neural-Network Quantum States optimized via natural-gradient methods and neural importance sampling, to resolve the ground-state and low-energy properties of the anisotropic triangular-lattice Hubbard model.

This project aims to develop machine learning potential for molecular dynamics simulations of PET solvolysis under sub and supercritical water conditions.

The most unpredictable space weather events are substorms, periods of energy loading and explosive release within the nightside of the Earth, resembling solar eruptions.

By developing a domain-specific AI co-pilot, mAiEnergy aims to enhance citizen engagement in the energy transition by providing context-aware insights on renewable energy, efficiency measures, flexibility markets, and smart grid technologies.