Awarded Projects

This follow-up project tries to improve the probabilistic precipitation nowcasting system developed during the previous EuroHPC allocation and prepares it for operational use over the Nordic radar domain.

Proton fast ignition, a variant of laser-driven inertial confinement fusion potentially providing much higher energy gain with lower driver energies, will be studied with the aim of optimising the conversion efficiency of the short-pulse laser into proton beam energy.

The success of this approach relies on the ability to generate a proton beam with the right characteristics to heat and ignite the isochoric DT fuel assembly. At the same time, a quasi-spherical robust and effective DT fuel compression around a re-entrant cone needs to be achieved.

This project proposes and trains haloCT-GAN, a novel generative adversarial network architecture that learns to synthesise anatomically coherent 128³-voxel CT volumes from a single frontal chest X-ray, bridging this fundamental imaging gap.

In this proposed project, we plan to use different cutting-edge simulation techniques to unravel the role of antibody binding site rearrangements in antigen binding and investigate the influence of different heavy and light chain pairings.

Particle acceleration in a short distance remains a high-priority research focus on fundamental physics and developing new technologies. Since introducing chirped pulse amplification, the research community has developed new methods of accelerating charged particles in a short distance.

Accelerating electrons and positrons to high energies over short distances is one of the most important open questions in physics.

The interaction of ultraintense laser pulses with the matter, in the form of solid foils, foams, or gas streaming from a nozzle or gas cell, leads to the generation of positively charged ions and electrons, and secondary X- or gamma-rays, positrons, fast neutrons or even more exotic particles.

Extreme-scale models have sparked a paradigm shift in natural language processing. Trained on broad plentiful data, they exhibit powerful emergent capabilities as they scale (e.g., zero-shot generalization), and their nearly universal effectiveness has led to a cornucopia of applications.