208 engineering-computation-"https:"-"https:"-"https:"-"https:" positions at ETH Zurich
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modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural systems and develop predictive tools
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Candidates should have a PhD in Electrical Engineering, or a closely related field. A strong practical embedded hardware background is essential, including experience in several of the following
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technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university
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using clinical and sensor-based data. The position provides exposure to multimodal clinical and laboratory-based data acquisition, collaboration with computer scientists, engineers, clinicians, and
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100%, Zurich, fixed-term Prof. Dr. Sereina Riniker's group for Computational Chemistry at the Institute of Molecular Physical Science is interested in the development of methodology for classical
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. Its mixed-methods research programme integrates macro-level theory with detailed, contextualised fieldwork across world regions, combines qualitative, quantitative, and computational approaches, and
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modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural systems and develop predictive tools
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engineering, and theoretical electromagnetism. The project offers the opportunity to work closely with internationally recognized researchers while contributing to an ambitious research program at the forefront
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research, providing training and directly supporting those engaged in peace practice and conflict transformation processes. We are seeking someone to work as a Program Officer in our “Mediation Support
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Computer Science, Electrical Engineering, Robotics, or related field You bring: A strong foundation in machine learning, including classification and regression, model evaluation, representation learning, and