Awarded Projects

The project team proposes a suite of multilingual continual pretrained Dense and Mixture-of-Expert (MoE) models at different size tiers for different types of workloads that have different inference compute constraints.

With their current computer vision and natural language processing models, the project already manages to process 30% of incoming claims automatically.

By combining structural information with mutation-aware modelling at scale, this project aims to deliver a significantly more accurate and biologically grounded variant effect predictor, supporting advances in biomedical research, precision diagnostics, and large-scale functional genomics.

Motivated by the needs of the Horizon Europe project LUMINOUS, the team proposes a novel way to develop Multimodal LLMs for low-resource languages (LRL), adapting a strong and open English-centric MLLM.

This project intends to train a multimodal transformer based model incorporating text, audio, and image modalities for the scandinavian languages (Swedish, Danish, Norwegian, and Icelandic) and English.

Coastal regions are becoming increasingly populated and industrialized, with nearly one-third of humanity residing within 100 kilometres of the coast.

This focused project directly supports the broader EuroLLM and OpenEuroLLM initiatives by addressing a critical bottleneck – the availability of high-quality pre-training data.

This focused project directly supports the broader EuroLLM and OpenEuroLLM initiatives by addressing a critical bottleneck – the availability of high-quality pre-training data – distinct from the large-scale model training requested in our parallel Extreme Scale proposals.

The project focuses on developing a revolutionary generation of universal snakebite antidotes, answering an urgent WHO health priority.