Awarded Projects

This project proposes to train and validate a novel language model architecture based on energy-based neural networks that provides native explainability — a critical requirement under the EU AI Act for high-risk AI systems.

This project will explore a novel, scalable, and cost-effective approach to instruction tuning and alignment of existing LLMs to new languages.

This research aims to deliver a scalable, edge-ready blueprint for sustainable, robust artificial intelligence.

This proposal focuses specifically on scaling Video-Panda.

This project aims to establish data-compute-model scaling laws for multimodal systems tailored to document understanding.

The national libraries of Norway and Sweden collect and preserve nearly everything that is published in their respective languages. Both organizations have used these collections to train and release open access AI models that have seen widespread use with millions of combined downloads.

The objective of this project is to study search algorithms in the context of two-players stochastic games.

The primary objective is to evaluate the impact of self-interacting dark matter on alleviating discrepancies between simulation results and observations of galaxy-galaxy strong lensing in cluster environments.

Understanding how merging binary neutron stars (BNSs) can launch powerful relativistic jets and, in turn, produce short gamma-ray bursts (SGRBs) remains a major theoretical challenge.