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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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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
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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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We are looking for a motivated PhD student to explore the muon spin spectroscopy for perovskite solar cells research. This position is part of Muoniverse, a Swiss National Centre of Competence in
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will be enrolled in the electrical engineering doctoral program at ETH Zurich. The project is embedded in the National Competence Center for Research Muoniverse and funded by the Swiss National Science
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. The group Multiomics for Healthcare materials at Empa, St. Gallen generates and integrates multi-modal biomedical datasets with the aim to inform development of new sensors, support nanoparticle-based
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doctoral program at ETH Zurich. The project is embedded in the National Competence Center for Research Muoniverse and funded by the Swiss National Science Foundation. Muoniverse connects over 30 research
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Muoniverse, a Swiss National Centre of Competence in Research (NCCR) dedicated to advancing muon science across particle physics, quantum materials, and applications ranging from energy research to cultural
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) and statistical modelling (e.g. mixed models, ANOVA, power analysis). Curiosity and a willingness to learn are a pre-requisite and familiarity with physiological data (e.g., heart rate variability) is a