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Understanding and simulating strongly correlated quantum systems is a central challenge in modern physics, with applications ranging from high-temperature superconductivity to quantum computing.

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.

This project aims to explore and refine methods for the next-generation models for drug discovery

This project proposes to develop and evaluate a next-generation autoregressive visual generative model that combines the strengths of autoregressive transformers, large language models (LLMs), and diffusion models.

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).

Norway’s complex terrain, rugged coastline, and high-latitude environment make accurate 0–6 h weather prediction particularly challenging, while societal needs for reliable short-term forecasts, ranging from flash-flood warnings to renewable-energy management, continue to grow.

This project seeks to measure the mesonic non-singlet screening masses projected onto the first non-zero Matsubara frequency across a previously-unexplored temperature range, from 1 GeV to 160 GeV, with sub-percent accuracy in the continuum limit.

The safe disposal of high-level radioactive wastes arising from nuclear power generation requires that the waste is isolated from the geo-/biosphere for extended timescales, in order to protect humans and the environment against ionising radiation.

This project focuses on enhancing the function-calling capabilities of large language models (LLMs) by connecting them to curated APIs using OpenAPI specifications.