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applications for a project scientist (post-doc) position at the GeoHydrodynamics and Environment Research group (GHER, https://www.gher.uliege.be ) of the University of Liège in Belgium. The candidate will work
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collaboratively in an international research environment Preferred qualifications: Proficiency with a relevant data-analysis environment, preferably Python or R, and SPSS Background in comparative higher education
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Excel. Intermediate level R, Python, and SQL. Familiarity with SPSS, STATA, Dedoose, or similar software. Previous experience in data visualization with business intelligence software such as Power BI
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for managing and analyzing large, complex datasets to support research projects in these fields. This work will likely involve statistical analysis using programming languages such as Stata, R and Python
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additional years Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8079-TATGIR-024/Default.aspx Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Research
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research on perovskites and/or organic semiconductors Demonstrated experience in global and deconvoluted data fitting, programming in Python and Matlab, as well as electronic structure modeling Intercultural
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University of Ottawa | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 2 months ago
discipline quantitative connexe; cinq années d'expérience en enseignement postsecondaire; excellente connaissance de l'informatique statistique (R ou Python, SAS); bilinguisme (français et anglais). Poste à
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computing, obtained within a maximum of 7 years; application of machine learning and deep learning methods to remote sensing images; proficiency in programming (R, Python, or similar); ability to work
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, although holding a PhD in other areas, have a scientific curriculum in one of these areas may also apply. 3. Candidates must have knowledge of markup languages (XML/TEI); programming languages (Python/R
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analysis. Excellent programming skills in Python and experience with deep-learning frameworks (preferably PyTorch). Experience developing, training, validating, and optimising machine-learning models