18 coding-"https:"-"Prof"-"FEMTO-ST"-"https:"-"https:"-"https:"-"https:"-"Inserm"-"Inserm" positions in Switzerland
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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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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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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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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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related field Solid background in physical optics, experience in interferometry and/or cryogenic optics is an asset Extensive experience with optical design and simulation tools (e.g. Zemax, Code V, FRED
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will be responsible for a range of research tasks, including literature search, data curation, coding for systematic literature reviews, data analysis, and miscellaneous administrative activities (e.g
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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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mechatronics systems is a plus Excellent coding skills in python, ROS, and RL&IL pipeline experience on simulator and training libraries. Knowledge on C++ is a plus Experience with Physics simulators such as
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programming skills (ideally Python) and commitment to clear, reproducible, well-structured code Working knowledge of SQL and/or NoSQL databases (and motivation to deepen your expertise) Strong analytical
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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