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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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modelling, designing, optimizing, and experimentally validating new system architectures, as well as contributing to collaborative research activities, preparing project deliverables, assisting with
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information obtained with different methods to generate a full picture of the aerosol properties Evaluating the results in terms of applications to satellite cal/val and/or model evaluation Publishing results
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(products, projects, business models). Develop strong knowledge and deep understanding of the local and regional energy landscape with strong focus on the industrial heat. Organizing workshops Preparation