19 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "U.S" "St" uni jobs at ETH Zurich
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relevance to extraterrestrial environments and future energy strategies. For more information on this project, see: https://copl.ethz.ch/research/research-projects/2025-saar.html Job description The PhD
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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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independently while collaborating with multidisciplinary teams Workplace Workplace We offer We offer a full-time research internship within our group as part of our collaboration with the ICAMS group in St
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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assistant, you will collaborate closely with a doctoral researcher and contribute to the screening and coding of academic papers (Note: Coding in this role refers to categorizing information from research
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future . Curious? So are we. We look forward to receiving your online application by May 31 with the following documents: A short motivation letter (max. 1 page) Your current CV A brief portfolio or code
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experience and interests (max 1 page) Transcripts of all degrees (in English) Links to code repositories, publications, thesis (if possible) or projects (if available) Contact details of 2 relevant reference
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code, projects, etc) Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. For further
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) Unsupervised machine learning and deep learning methods Analysis, visualization, and interpretation of learned design spaces Contributing to research outputs (prototypes, publications, open-source code) Profile
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community Contribute to R data publication packages washr and fairenough Profile You care about data and code being concise and easily reusable You know how to use standard data science tools (Git, GitHub, R