217 computer-science-quantum-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at ETH Zurich
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well as metabolic, neurodegenerative, autoimmune, and infectious diseases. Job description PhD projects will combine catalyst engineering, computational simulation, bioelectrode device fabrication, and electrical
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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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datasets The position is limited to two 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
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scientometrics Profile Background Master’s degree ideally in information science, library science, data science, computer science, or a comparable field; a PhD is an advantage Regardless of academic background
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dynamical systems, and machine learning, with applications to synthetic biology and biomolecular circuit design. Our research develops mathematical and computational frameworks for understanding and
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evaluate prototypes together with industrial partners Profile Required experience CH/EU/EFTA citizenship or valid Swiss work permit PhD in Engineering, Computer Science, Robotics, or related field Strong
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interpretation of explosion events in underground ammunition storage facilities. The position is embedded in a collaborative project with armasuisse Science and Technology and partners from industry and the Swiss
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The Strategic Communication Officer plays a central role in shaping NCCR Catalysis's positioning, both internally and externally. Working at the interface of science, strategy, and communication
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of emerging technologies balanced against their dark sides. Our research at the intersection of data, behavioural, and computer science draws on data from real-world and experimental settings The Centre for AI
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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