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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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to compliance with export-control and embargo legislation . The committee will also consider the applicant’s specific research topic as well as the thesis supervisor(s) the applicant would like to work with
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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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, biotechnological process parameter control, as well as material preparation and characterization techniques Profile We are seeking a highly motivated candidate with: Master’s degree in material science/engineering
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joining the team? ETH Zürich is one of Europe’s foremost technical universities, located in the heart of Switzerland. The PhD candidate will be positioned at the Department of Mechanical and Process
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PhD student position on Optical Control over Topological Phases of Matter (ERC Starting Grant) Starting April 2026 (or as agreed) The newly-established “Quantum Opto-Electronics” research group
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Starting Grant project, which aims at controlling strongly correlated topological phases of matter with light. The successful candidate will become an essential member of our fast-growing research team