214 computer-science-intern "https:" "https:" "https:" "https:" "Central European University" positions at ETH Zurich
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for mechanical failure. Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics. We work across a broad range of
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skills which could be relevant include rendering, computer science (with a specific focus on front-end interface development) Fluency in English is required for this position and German is a plus You
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close collaboration with experimental and clinical partners, and we are embedded in one of the strongest AI and computer science environments worldwide. Learn more about our research and recent work
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develop predictive tools for mechanical failure. Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics
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. ENDOTRAIN will train a new generation of interdisciplinary experts who merge clinical endocrinology, artificial intelligence, data science, engineering, ethics and law into an integrated field of digital
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their recruitment date. The student will be enrolled in the structured PhD programme of the Department of Mechanical and Process Engineering or the Department of Health Sciences and Technology
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The Chair of Strategic Management and Innovation provides an inspiring, team-based research environment with a dynamic and international group of researchers. Our team has a strong track record of
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for mechanical failure. Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics. We work across a broad range of
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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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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