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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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thermoplastic feedstocks for FDM printing Programming of an existing hybrid FDM printing machine, including printing and milling devices Modelling and optimization of the debinding process Simulation-based design
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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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space. In the frame of an industrial project, we are looking for a postdoc holding a PhD in material science, physics or chemistry, with a clear link to X-ray based analytical methods, especially to X-ray
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, with a clear link to X-ray based analytical methods, especially to X-ray diffraction and if possible, X-ray imaging. We expect the candidate's enthusiasm for interdisciplinary work related to Space
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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 aim of the project is to extend cycle life by the controlled release of active lithium. Your tasks Develop lithiation agents to recover lost active lithium during operation of the battery Develop