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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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adaptable networks. Recently we have developed different types of inherently flame retardant dynamic networks for fire safe fiber reinforced composites and self healing coatings. The proposed position will
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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Contribute to open-source software and reproducible research artifacts Collaborate actively, fruitfully, and respectfully with the PIs, the HPC group, and partner institutions Contribute to teaching (max. one
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nanofabrication Our offer A highly specialized and technically unique research environment with world-leading, custom-built microscopy platforms Participation in a broad network of international collaborations
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, training, and participation in international research networks. A vibrant, international, and multidisciplinary environment that strives to be inclusive to people from diverse backgrounds. How to apply
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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development for PhD students and
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directly to the future operational exploitation of MTG data. Job description We are looking for a Scientific Programmer / Software Developer to join our motivated and interdisciplinary team. In this role