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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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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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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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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision of students Your
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Description As part of a new EU MSCA Doctoral Network ELEVATE (101227453), we are offering a PhD Position on “Modulating bursting activity in silicon neurons to control plasticity and attention”. Location