Awarded Projects

Large language models (LLMs) require immense resources for training and inference. Quantization, a technique that reduces the precision of model parameters, offers a promising solution for improving LLM efficiency and sustainability.

SynthesizeCT aims to go beyond the state-of-the-art by developing a physics-informed Generative AI (GenAI) model that synthesises 3D anatomical maps directly from PET acquisitions, completely eliminating the need for CT irradiation.

Density functional theory (DFT) is the primary workhorse in computational materials science.

The purpose of the proposed project T2ARC (Textual-to-Abstract Reasoning Compression) is to develop and test a new method for abstract reasoning with LRMs.

The question how solar storms impact a planet has both fundamental scientific importance and great social impacts for protecting our infrastructure from the most powerful solar storms.

The project aims to extend the application of VAR extending the class-guided textual prompt mechanism of VAR to textual inputs.

Turbulence characterises many physical phenomena and is key to astrophysical and cosmological scenarios. Despite its success in simulating astrophysical scenarios, the smoothed particle hydrodynamics technique (SPH) has historically struggled when simulating turbulence.

Exploring the standard model of particle physics and finding new physics beyond is in many cases limited by the lack of high-precision knowledge of low-energy QCD effects. The only known systematically improvable method to compute such effects from first principles is lattice QCD.

Exploring the standard model of particle physics and finding new physics beyond is in many cases limited by the lack of high-precision knowledge of low-energy QCD effects.