81 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "https:" "P" positions at ETH Zurich
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2026, with a 100% workload, based in Zurich, and is fixed-term for three and a half years. Working across sociocultural, political-economic, and theoretical contexts, the LUS Doctoral Program fosters
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to the research group’s teaching responsibilities, which involves teaching courses for ETH’s teacher training program in German. Further, the candidate should be prepared to co-mentor PhD students and to mentor
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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Each doctoral student will lead a coherent subproject within this broader research program: Position 1: AI for Computational Thinking Focuses on designing and studying AI-assisted programming
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, you will publish your results in peer-reviewed journals and present them at international conferences Profile You meet the requirements for a doctoral program at ETH Zurich and have an excellent
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study is of highest priority. The mission concept study will finalize the required satellite architecture, conduct key technical trades, and finalize the overall mission development plan and
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80%-100%, Zurich, fixed-term The Swiss National Supercomputing Centre (CSCS) develops and operates a high-performance computing and data research infrastructure that supports world-class science in
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scientific publication. Source Data extraction from PDF documents to compile metadata of projects funded under ETH Board Open Research Data (ORD) program Contribute to data packages generated by openwashdata
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for world class research in the field of robotic fabrication in architecture and construction. Job description We are looking for a highly skilled software engineer to work on the development of python
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-phonon coupling elements. With these, dedicated scattering rates can be computed and then used in quantum transport simulations. Down the line, we aim to pre-train a common GNN backbone model capable