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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.

This project aims to train a Medical-Vision Language Model (Med-VLM) that can process medical images and provide high-quality textual answers to medical questions in various languages.

Protein flexibility, motion and conformational transitions form the bedrock of biological processes.

This project proposes the creation of a novel 3B–7B parameter multilingual model that integrates a generative core with a distinct, retrievable memory and knowledge layer, mimicking brain-inspired cognitive processes.