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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological
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programming or scripting languages (e.g., Bash, R, Python) for data processing, statistical analysis, and bioinformatics workflows. Experience working in high-performance computing environments is an advantage
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quantitative analysis (e.g. structural equation, multilevel, epidemiological, genetic, or psychometric methods), and proficiency in statistical software such as R, Mplus, or Stata. UiO has developed a matrix for
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Experience with high-throughput sequencing omics data analysis Proficiency in programming with Python, R, or C++ Candidates without a master’s degree have until 31 August 2025 to complete the final exam
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in quantitative methods (reflected in courses and/or research experience) Proficiency in R, Python, or similar programming languages (or strong skills in another statistics software) Knowledge about
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for Bayesian inference Documented experience with programming in either Python or R. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system Fluent
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Experience with rodent development, colonies maintenance Fluent oral and written English communication skills Python, Matlab, R or other relevant programming language skills Experience in the following will
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industries and leading R&D institute RISE Fire Research in Norway and R&D partners in Sweden, allowing the candidates to provide with solutions to real challenges with implementation potential. Are you
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following computing skills will be considered an advantage: Natural Language Processing and LLMs; R; Python. Applicants must be fluent in English. Applicants who have completed their education outside
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computational skills (using R, modelling software, working on a remote linux-based server) and experience in analyzing Next Generation Sequencing data, including PCA, outlier analysis, GO-term enrichment analysis