172 computational-physics "https:" "https:" "Institute of Computer Science" positions at ETH Zurich
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energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
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Contribute to supervision of students and interns where appropriate Profile PhD in climate science, atmospheric science, computer science, data science, physics, applied mathematics, remote sensing, or a
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
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could have impact on diseases with inflammatory and metabolic components. Job description As a PhD student, you will: perform biological and physical characterization of the glycopolymers in different
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environmental assessment, as well as policy and regulation. Job description The doctorates will be conducted within the Einstein School’s doctoral program. The positions are for three years, starting in September
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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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. Joël Mesot. The closing date for applications is 22 February 2026. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://ethz.ch/en/the-eth-zurich/working
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environment where philosophy meets data science, public health, medicine, and law? At the Health Ethics & Policy Lab (https://bioethics.ethz.ch ), you will join a team committed to shaping responsible
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datasets The position is limited to two years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep
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years. Profile University degree (MSc or PhD) in data science, computer science, physics or a related field Experience in training and validating large-scale deep-learning models on distributed systems