165 computational-physics "https:" "https:" "https:" "https:" "Caltech" positions at ETH Zurich
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, Switzerland [map ] Subject Areas: Computer Science / Distributed Systems and Networking , Networking , Networking and distributed systems Appl Deadline: 2026/01/08 11:59PM (posted 2025/11/10, listed until
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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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. 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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understanding of the aging of solid insulation under mixed-frequency medium-voltage stress, see https://doi.org/10.1088/1361-6463/acd55f for a relevant example research work of our team in this area. Profile
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received by 1 November 2025. Note: as an option, one of the four reference letters can be about teaching. For more details, please see the website: https://math.ethz.ch/fim/postdocs.html Application
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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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computational workflows to design novel AAV capsids Dry-to-wet: lead the computational design process and actively participate in wet lab validation of your designs (e.g., library construction, viral production
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to work at the interface of quantum optics, quantum information science and quantum many-body physics. Led by Prof. Wenchao Xu , the EQE group develops programmable quantum systems based on neutral atoms
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10%-30%, Zurich, fixed-term We are looking for motivated student research assistants to join our project “Adaptive Physics-Informed Neural Operators with Reduced-Order Modeling for Complex Dynamical