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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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to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems that act as autonomous micro
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integrated in a removable smart denture. The project targets the integration an advanced micro-system comprising sensors, micro-fluidics, and a sophisticated drug delivery system. The complex 3D printed
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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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probe microscopy. Our research focuses on using single electron spins in diamond as sensors to explore magnetic phenomena at the nanoscale. This doctoral project will center around the development and
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, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to optimize other thermal processes
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., PyTorch, TensorFlow) and medical image analysis libraries (ITK, VTK, Slicer3D, MONAI) Experience with hardware-software integration, including encoders, sensors, and safety aspects of robotic workflows
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some products decay faster. For that purpose, we develop digital twins of the cargo, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end