36 evolution "https:" "https:" "https:" "https:" "https:" "U.S" "St" "St" positions at Empa
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of laboratory technicians specializing in chemistry is also based in the analytical center. Your tasks Development of a measurement cell for the online detection of transition metal ions, fluorides, and
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Membranes and Textiles laboratory, interdisciplinary teams work on the development, integration and validation of novel sensing systems - particularly for textile applications. The focus lies
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. Empa is a research institution of the ETH Domain. For an applied research project, we are seeking a highly motivated Postdoc interested in the development of a hybrid AM manufacturing process for silicon
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plenty of possibilities for personal and professional development. Fully funded four-year PhD position at Empa in Dübendorf, starting from September 2026. Joint supervision/research resources and
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opportunities for professional and personal development. Start date is May 2026 or upon agreement for a duration of 2 years. We live a culture of inclusion and respect. We welcome all people who are interested in
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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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. Empa is a research institution of the ETH Domain. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and
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development. The project is in close collaboration with a large enterprise and focusses on the development of high-performance materials and materials systems based on, among others, silica aerogel. Key
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. Empa is a research institution of the ETH Domain. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive