215 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"CUBO"-"CUBO" positions at ETH Zurich
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, engineering, physics, or a related field, and with strong interest in the cryosphere. The successful candidate has experience in computational data analysis or numerical modelling. You are eager to work
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Architecture and Geohistorical Practice, led by Assist. Prof. Aisling O’Carroll, is engaged in research and teaching activities on the Master of Science in Landscape Architecture (MScLA) program. The Chair
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100%, Zurich, fixed-term The Membrane and Interfacial Science Lab in the Department of Mechanical and Process Engineering (D-MAVT) at ETH Zürich designs materials and processes that enable more
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. Profile Hardware Realization: Translate abstract architectural requirements into functional ViViD-AFM V2.0 mechatronic hardware. Precision Engineering: Design and implement sub-nanometer control systems and
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operations that are yet to be fully understood. In this context, it is evident that the operation, control, and planning of power systems will soon be pushed to their limits. Therefore, new computational
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asset. You are fluent in English and enjoy working in an international, diverse, and interdisciplinary team. Degree in Computer Science or a related field Several years of experience as a
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100%, Basel, fixed-term The highly competitive Bio-Engineering Systems for Therapeutics (BEST) postdoc program, part of the Next-gen Bioengineers initiative, is run jointly by ETH Zurich and Roche
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university and in developing research and teaching partnerships with computer science faculty and researchers. We are also interested in candidates who value engaging publicly on topics of the ethical, social
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, Biostatistics, Software Engineering, Systems Operations, and Screening & Lab Automation. Embedded in this multi-disciplinary environment, the Clinical Bioinformatics group translates computational biology into
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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