156 computer-programmer-"https:"-"FEMTO-ST"-"U"-"Inserm" "https:" "https:" "https:" "https:" "UCL" 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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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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student (hosted at SLF Davos and the University of Zurich) and a dedicated software engineer (based at SLF Davos). The PhD will be supervised by Dr. Michele Volpi and Prof. Johan Gaume at ETH Zurich’s
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100%, Zurich, fixed-term The doctoral program at the Institute of History and Theory of Architecture (gta), ETH Zurich, offers two fellowship positions to start on 1 October 2026. This program
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class research in the field of robotic fabrication in architecture and construction. The Chair of Timber Structures advances education and research in timber engineering through the Program for Excellence
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100%, Basel, fixed-term The highly competitive the Bio-Engineering Systems for Therapeutics (BEST) postdoc programme, part of the Next-gen Bioengineers initiative, is operated jointly by
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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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100%, Zurich, fixed-term The Distributed Computing (DISCO) Group is a research group at ETH Zurich, led by Prof. Dr. Roger Wattenhofer . We are interested in a variety of research topics on new and
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component of solid-state transformers (SSTs). Such SSTs are required, for example, in future AI data centres, where power consumption per computer rack increases to levels of several hundred kilowatts or even
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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