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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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computationally and developing scientific software. Experience in Python is highly recommended, additional knowledge of performance-oriented modeling frameworks, either based on Python (e.g., JAX, Pytorch) or other
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-climate interactions. For this purpose, it has developed its own dedicated global model SOCOL, which can interactively treat all processes and major feedbacks related to the ozone layer and atmospheric
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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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Your position The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical
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Your position The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical
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have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high-throughput