201 distributed-computing-"Multiple"-"Humboldt-Stiftung-Foundation" positions in Switzerland
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The research group of Distributed Ledger Technology and Fintech (Prof. Fabian Schär) of the Faculty of Business and Economics at the University of Basel invites applications for a PhD position with
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technologies promise to revolutionize multiple branches of science by solving problems that cannot be tackled by classical systems. While efficient and large-scale quantum computers are still far from being
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predict environmental distributions of health-impacting trace elements to prevent health hazards, today and in the future. The position is offered in the framework of a recently funded SNSF project with
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engineering, computer science, data science, or a closely related discipline Have an excellent academic record Have strong analytical skills Be passionate about sustainability, energy, and public policy Be
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institutions, to build a framework for discovering, sharing and executing data and algorithms in a distributed environment. The main technology will be Python, though Scala and Typescript knowledge are a plus
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specifically to XRD-PDF (pair distribution function). The project is linked to crystalline and amorphous alloys, and in an industrial context to process design by establishing the structure-property relationship
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of computational and statistical genomics, and bioinformatics. Cattle are an interesting «model organism» to study inherited genetic variation and the molecular-genetic underpinnings of complex traits and dieseases
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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conduct rigorous experiments in humans, with a particular emphasis on synchronizing data from multiple sources Experience setting up complex biosignal recordings is an advantage Strong communication skills
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during field measurements, capture high-resolution imagery of cloud droplets and ice crystals to determine their size distributions and types. The resulting large datasets (often several terabytes