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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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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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of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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) influence system performance and trade-offs. The research will combine analytical modelling with data-driven and AI-based methods, for example for scenario generation or uncertainty exploration. The PhD will
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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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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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activation and micromechanical modeling Progressive damage modeling of reinforced FRPs Mechanical characterization and fracture experiments Complete a PhD thesis at ETHZ Your profile Highly motivated and
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of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
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, technologies and systems. The ERAM group within TSL have great experience in SSbD, especially in combining different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi