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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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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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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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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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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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heritage. Muoniverse brings together 30 research teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates
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with a large and high-profile network that includes members of both academia and industry. Empa is committed to diversity, equity, and inclusion. We strongly encourage applications from female
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, geography, and artificial intelligence to develop coherent and resilient approaches for urban energy infrastructures under land-use constraints such as No Net Land Take. The consortium comprises four
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
scientific objectives include: Elucidating how monomer structure, solvent choice, and oligomer formation govern polymerization pathways, phase behavior, and network evolution Understanding polymer–solvent