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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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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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secondments at UCL – University College London and at University of Leeds (UK). We are looking for you! Do you want to be trained to develop multi-level thrombosis risk prediction models by integrating insights
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are looking for you! Do you want to be trained to develop multi-level thrombosis risk prediction models by integrating insights from cell-, thrombus-, and organ-level perspectives? While being part of a