Awarded Projects

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.

The Global and Modular Beyond-the-Standard Model Inference Tool (GAMBIT) is an open-source code for performing the most complete and rigorous assessments possible of new particle physics theories.

Ocean-atmosphere fluxes play a critical role in the evolution of climate. A better and more quantitative understanding of the physical mechanisms governing the exchanges of mass and gases across the air-water interface is strongly needed.

Massive Black Holes (MBHs) populate the centres of most, if not all, galaxies with total masses greater than approximately 1e12 solar masses.

This project makes use of a novel approach for numerical lattice simulations of the strong nuclear force.

Adding polymers to a Newtonian solvent alters the turbulence, ultimately resulting in the macroscopic effect of drag reduction (DR) in a turbulent wall-bounded flow.