Awarded Projects

Traditional large-scale AI models, operated by hyperscalers or private companies directly (ie. OpenAI and Anthropic), often fall short in meeting these sector-specific requirements while raising concerns about data privacy, vendor lock-in, and cost-effectiveness.

This project, led by Bitnet, aims to benchmark, design, and scale novel Neural Operator-based frameworks for predictive maintenance and digital twin applications in energy-critical systems—extending from hydrogen fuel cells to electric vehicle (EV) batteries.

Large Language Models have revolutionised work with breakthrough capabilities. They can perform tasks without explicit training, making them incredibly powerful.

Understanding the origin of globular clusters (GCs) is at the frontier of astronomy.

This project, "Bio-Plausible Spiking Neural Networks for Energy-Efficient Automatic Speech Recognition," addresses the critical challenge of high-energy consumption in deep learning models for speech processing.

Plastics are important polymeric materials with versatile applications, such as boat hulls/decks, tanks, pipes or gratings, and bottles, but unfortunately, they contribute massively for severe plastic contamination worldwide.

The project outlined here aims to uncover firstly the formation pathways of MBHs and secondly to track the evolution and growth of MBHs in very early galaxies as the MBHs grow, merge and settle towards their host galaxy's centre.

In this study, 3D dynamic earthquake simulations will be used to create a large database of synthetic ground motions in Israel, along the active Dead-Sea Transform plate boundary, explicitly accounting for the covariance in the input parameters.

The interaction between bubbles and turbulence is a crucial aspect of many natural phenomena and industrial processes.