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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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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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qualified candidate holding a Master’s degree in Engineering, Energy Systems, Applied Mathematics, Operations Research, Physics, or a related field. You bring a strong analytical background and experience
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with project partners and disseminate results through reports, visualisations, and scientific publications Your profile You hold an M.Sc. in Data Science, Computer Science, Engineering, Physics
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of students Your 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
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approaches. The PhD will develop and apply optimization-based energy system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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academic and industry partners. Your profile You are a highly motivated and talented candidate with a Master’s degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related
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in Mechanical or Aerospace Engineering, Physics, Computational Science, or a related discipline. The candidates should have: Solid programming skills (Python, MATLAB, or C++). Knowledge of the OpenCV
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living