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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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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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, antibiotic resistance genes, VOCs and PFAs. Investigations on electrode materials, manufacturing processes, signal amplification and modulation are underway. We strive to strengthen environmental applications
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well as external academic and industry partners. Your profile You are a highly motivated and talented candidate with a Master’s degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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academic and industry partners. Your profile You are a highly motivated and talented candidate with a Master’s degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related