202 electronics-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich
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of psychology, technology, and human–computer interaction). We welcome candidates with a strong interest in fundamental research and, ideally, prior experience in VR research. The project duration is set for
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, [email protected] (no applications). Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered
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knowledge and technology from academic research into the Swiss mechanical, electrical, and metal industries. Project background We are looking for a motivated intern to support the development
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100%, Zurich, fixed-term We are seeking a skilled engineer to join the Apertus evaluation effort. The ideal candidate will build and operate the evaluation codebase and pipelines that inform our
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. Questions regarding the position should be directed to [email protected]. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or
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100%, Zurich, fixed-term The Optical Materials Engineering Laboratory (Prof. David J. Norris) in the Department of Mechanical and Process Engineering (D-MAVT) at ETH Zurich investigates
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experimental challenges Applicants should hold, or be close to obtaining, a Master’s degree in physics, electrical engineering, photonics, materials science, or a related field The position will involve
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. Applications via email or postal services will not be considered. We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. About ETH
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100%, Zurich, fixed-term We are seeking a skilled engineer to own the technical release path of Apertus models. The ideal candidate will integrate Apertus into the open-source inference ecosystem
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modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural systems and develop predictive tools