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Field
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appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other
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using R for statistical analysis and in statistical modeling. Sound working knowledge of unix shell, high performance compute clusters and git. Willingness to learn and use a scripting language (e.g
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related discipline and have begun to establish a strong research profile, evidenced by a strong, well-cited publication record. Proficient in Python, R, BASH and/or other relevant programming languages, you
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or electrophysiology, and proficiency in MATLAB/Python/R are desirable but not required. The position is part of a well-funded research program and will provide the opportunity to produce high-impact publications and
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groups/minorities would be an added strength. Preference will be given to applicants with knowledge of analyzing quantitative research data using advanced statistical tools (e.g., MPlus, R). Applicants
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estimators, or machine learning) or other advanced statistical modelling. Advanced programming skills in Stata, R, Python or a similar software. Strong academic background with publications in international
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practices in different research communities; Teach lessons and workshops such as data processing and analysis using R or Python, tools and best practices for data management, computational best practices
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chronometabolic research Analysis and quantification of circadian rhythms by rhythm analysis software (Cosinor, JTC cycle, R etc.) Rhythm analysis in human 24-hour time series data and omics data Writing
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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data using MEFISTO. Nature Methods (2022) Kleshchevnikov, Vitalii, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology (2022) Argelaguet, R., et al. Multi