167 coding-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "University of Essex" positions in Switzerland
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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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(FOPH) of Switzerland and the Centers for Disease Control and Prevention (CDC) of the United States. We value good software engineering practices. We perform code reviews, have automated testing pipelines
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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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of research excellence through peer-reviewed publications Evidence of comittment to reproducibity in bioinformatics through the use of Code Repositories Evidence of ability and experience in handling
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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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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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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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sustainability (max. 0.5 page) • a list of publications and other research outputs, including datasets, code, etc. • a copy of their PhD certificate Please note that page limitations do not include references
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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