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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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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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that will shape your research career. Your profile We are looking for 2 highly motivated PhD students with a strong analytical background and an MSc degree in Physics, Computational Chemistry, Materials
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printing methods. The project is in collaboration with the University Bern. Your tasks Explore commercial and synthesis piezoelectric ceramic powde Explore sintering additives for the cold sintering process
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the University of Zurich. Your profile Master's degree in Earth Sciences, Geography, Physics, Environmental Sciences, Meteorology, or a related field. Passion for atmospheric remote sensing, chemical
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Master's degree in Earth Sciences, Geography, Physics, Environmental Sciences, Meteorology, or a related field. Passion for atmospheric remote sensing, chemical transport modelling, and data science. Solid
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profile University Master's degree in process engineering, chemical engineering, mechanical engineering or materials science with some (CFD) experience. Good technical knowledge and/or high interest in
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passionate about working in an interdisciplinary field, and fulfil the following criteria: Completed Master`s degree in physics, chemical engineering, biomedical engineering or similar, you have strong hands