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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.

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.

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

Digitalization is a longstanding goal of the EU, with varying degrees of progress among member states. Large language models are a promising catalyst for this process, but the current landscape presents significant challenges.

Multimessenger (MM) astrophysics promises to answer some of the most intriguing open questions in Physics, including the nature of gravity, the properties of nuclear matter, the origin of the heaviest elements.

The properties of atoms, molecules and solids could all be computed reliably if we were able to solve the many-electron Schrödinger equation quickly and accurately enough.

The muon, a short-lived cousin of the electron, has provided a longstanding discrepancy between the standard model of particle physics and experimental measurements.

We request CPU and GPU hours of computational resources for calculating the equilibration of quark-gluon plasma (QGP) in high-energy heavy-ion collisions (HICs).