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This project is the first validation phase of a deliberately staged, three-part Large Scale AI Factory program. The structure is designed to allow EuroHPC to commit large-scale compute incrementally, ensuring confidence in the methodology.
This project will develop a workflow to discover and engineer RNA-driven retroelement-derived gene writers. Building on the team's preliminary LINE-1 work, where genome mining, protein language models and reinforcement learning enabled the discovery and design of hyperactive ORF2p variants.
DigiFarm is one of Norway's leading ag-tech startups, established in 2019 who's core focus is on developing deep neural network models in agriculture leveraging SatEO data.
Numerical weather prediction (NWP) plays a critical role for European societies to avoid weather-induced harm and improve benefits, e.g. in the agricultural sector or for renewable energies.
Accurate medium-range weather prediction is of critical importance for European societies to prevent weather-induced loss of life and economic damages.
This project will develop an AI-driven multiscale workflow for transferring atomistic knowledge from universal machine-learning interatomic potentials (uMLIPs) to mesoscale phase-field simulations andrepresentative atomistic mechanical validation.
The study will couple committee-based active learning with synthetic nanoreactor simulations that drive QD surfaces into reactive, high-uncertainty regimes and trigger on-the-fly DFT relabelling.
ALETHEIA is an ambitious research and innovation project focused on training large-scale foundation models that integrate cognitive AI principles with human behavior modeling.
This project focuses on developing AI-assisted features for the WebRatio low-code platform by fine-tuning a Large Language Model (LLM) to generate IFML models in XML format, leveraging WebRatio's proprietary codebase.
Thermodynamic modelling routinely guides the research and development (R&D) of new materials and the CALPHAD (CALculation of PHAse Diagrams) method is one of the approaches of choice because of its accuracy achieved with a modest computational effort.