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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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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain. Empa’s Laboratory Materials for Energy Conversion...
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Materials science and technology are our passion. With our cutting
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of the structure – property relationship in applications such as biomedicine and space. In the frame of an industrial project, we are looking for a postdoc holding a PhD in material science, physics or chemistry
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. Empa is a research institution of the ETH Domain. The Biointerfaces Laboratory is offering a Postdoc position focused on the development of in vitro simulation models for performance evaluation
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30 Dec 2025 Job Information Organisation/Company Empa Research Field Biological sciences » Biology Biological sciences » Other Computer science » Programming Computer science » Other Medical
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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required. Our offer We offer this postdoc position at a research institution with excellent infrastructure and broad interdisciplinary surrounding. Position starting from June 2026 or upon agreement. We live
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: use of thin steel membranes to investigate hydrogen uptake, diffusion, and trapping under representative microstructural and stress conditions. The three postdocs will collaborate closely across
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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