184 high-performance-quantum-computing-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich
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analysis in Python is advantageous Previous experience with imaging (especially computed tomography) and/or image analysis is advantageous Excellent communication skills in English (oral and written) are a
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devices from motors and actuators to sensors, memories, and emerging paradigms for computation. Despite their technological importance, many fundamental aspects of their behavior remain elusive, especially
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Master’s degree in Computer Science, AI, Machine Learning, Mathematics, Electrical Engineering, or a closely related field; or Master’s degree in Medicine (MD) with strong Python skills and some ML
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and Health at the Department of Health Sciences and Technology at ETH Zürich (Hönggerberg Campus); regular group and 1-1 meetings; the possibility to lead your research project with a high degree of
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if you are currently enrolled as a student and required to complete a compulsory internship as part of your Master’s studies You have excellent written and spoken English skills (C1); German is an
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from material development, structural analysis with x-ray techniques, to digital modeling and simulation. The group also performs research on optoelectronics that are based on nanoparticles, offering
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contribute to the development and optimization of the training pipelines for these large-scale models, working at the intersection of cutting-edge research and high-performance computing to advance
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university degree (Bachelor’s or Master’s) in a relevant field; degrees in technical fields (computer science, mathematics, physics, engineering etc.) are a plus Fluency in English and German; French is a plus
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The NOMIS Foundation–ETH Fellowship Programme supports postdoctoral researchers at ETH Zurich within the Centre for Origin and Prevalence of Life (COPL). The programme is intended to foster
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, ACHIEVE consortium The primary focus of this role is to serve as the Knowledge and Technology Transfer (KTT) Expert within the ACHIEVE consortium as part of the SWEET funding programme of the Swiss Federal