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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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Materials Science group at ETH Zurich within the framework of the Marie Skłodowska-Curie Actions – Doctoral Networks (MSCA-DN) RE-Fibre project . Project background of the Re-Fibre Project RE-Fibre is a
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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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PhD positions available for highly motivated early-career researchers to be part of the new MSCA Doctoral Network – SHIELD SHIELD Doctoral Network offers an exciting opportunity for 16 early-career
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that explicitly incorporates protein–ligand dynamics. You will be responsible for: Designing and implementing innovative deep neural network models. Integrating physical principles and molecular modeling knowledge
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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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Marie Skłodowska-Curie doctoral training network “SPACER", which is made up of 21 partners. A total of 17 doctoral candidates will work in this project over a period of 36 months. School: School
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This doctoral thesis is part of the European Marie Skłodowska-Curie doctoral training network “SPACER", which is made up of 21 partners. A total of 17 doctoral candidates will work in this project over a period
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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networks. Extension of the findings to microwave systems is also planned. The research outcomes are expected to be published in major specialized and broad-audience journals. Profile You are expected to have