384 systems-science-"https:"-"https:"-"https:"-"https:"-"U.S" positions in Switzerland
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Your profile The successful candidate holds a master in Biology, Biochemistry, Nanoscience or similar. Qualified interns will be considered. Expertise with drug screening assays, cell culture and
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. Empa is a research institution of the ETH Domain. The Coating Technologies Group in Empa's Laboratory for Surface Science & Coating Technologies performs physical vapor deposition (PVD) of innovative
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human therapies. The D-BSSE is the first truly interdisciplinary research department, uniting theoretical and experimental biology with advanced bioengineering and fostering close collaborations with
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-inspired human therapies. The D-BSSE is the first truly interdisciplinary research department, uniting theoretical and experimental biology with advanced bioengineering and fostering close collaborations
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, nanotechnology, and bioelectronics. The D-BSSE is ETH Zurich's first truly interdisciplinary research department uniting theoretical and experimental biology with advanced bioengineering and fostering close
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, nanotechnology, and bioelectronics. The D-BSSE is ETH Zurich's first truly interdisciplinary research department uniting theoretical and experimental biology with advanced bioengineering and fostering close
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will be enrolled in the electrical engineering doctoral program at ETH Zurich. The project is embedded in the National Competence Center for Research Muoniverse and funded by the Swiss National Science
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weather and climate science. Job description Further develop and train machine learning model in Anemoi with a focus on regional weather predictions Improve the system by integrating observation data
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knowledge and technology from academic research into the Swiss mechanical, electrical, and metal industries. The Machine Concepts research group at inspire AG is offering a position in the field of Industrial
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems