142 computer-security-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"St" positions at ETH Zurich in Switzerland
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, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU), the PhD student
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psychologists, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU
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for key program deliverables. Drive the development of high-impact deliverables, such as the annual report and the Phase III Outline Proposal. Coordinate input across projects, synthesize insights, and
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to commercial-grade systems, we are seeking a visionary Lead Architect to drive the design and development of the V2.0 system. This role is critical for establishing technical novelty, securing a new IP portfolio
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natural plant defenses and reduce pathogen entry into leafy vegetables, contributing to sustainable agriculture and improved food safety.
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. Profile Ph.D. in Nuclear Engineering, Physics, Chemical Engineering, or a related field. Experience with computational chemistry and simulation tools. Prior knowledge of nuclear safety, severe accident
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100%, Zurich, fixed-term The Clinical Genomics team led by Dr. André Kahles at the Biomedical Informatics Lab (BMI Lab), headed by Prof. Gunnar Rätsch, at ETH Zurich, is seeking a highly motivated
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of Bern). The position will be hosted at the Institute for Atmospheric and Climate Science at ETH Zurich and will be part of the NCCR CLIM+ programme which is funded by the Swiss National Science
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organised individual to support multiple experimental and computational research groups. The successful candidate will contribute to both a collaborative research network and institute-wide activities
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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