216 associate-professor-computer-science-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich in Switzerland
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or mechanical engineering, or CS Solid knowledge of computer vision and ML, particularly anomaly detection methods Experience with multimodal data (e.g., image + time series, sensor fusion) is a strong ad-vantage
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screening platforms, with a focus on skin tissue models, including fibrosis. We are embedded within the Tibbitt group, in the Macromolecular Engineering Laboratory . Project background The glycocalyx is a
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computational and theoretical investigation of damage evolution associated with cavitation in soft materials under high-rate loading. The work will focus on developing physics-based models that connect behavior
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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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, with deep experience in appearance reconstruction and material modeling. Required Qualifications MSc in Computer Science or related field Strong background in computer vision and/or computer graphics
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100%, Basel, fixed-term The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational
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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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, 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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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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policy, political economy, economics, sociology, computational social sciences, or a related field Strong knowledge of advanced quantitative methods is essential (e.g., econometrics, causal inference