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din. En cookie er ikke et program som kan inneholde skadelige programmer eller virus. Hvordan nettsiden bruker cookies Cookies er nødvendig for å få nettsiden til å fungere. Cookies hjelper oss å få en
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international research network and are provided with personal computers, printer access, extensive library resources, and a wide range of software. Further information on the Department of Economics and on
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din. En cookie er ikke et program som kan inneholde skadelige programmer eller virus. Hvordan nettsiden bruker cookies Cookies er nødvendig for å få nettsiden til å fungere. Cookies hjelper oss å få en
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din. En cookie er ikke et program som kan inneholde skadelige programmer eller virus. Hvordan nettsiden bruker cookies Cookies er nødvendig for å få nettsiden til å fungere. Cookies hjelper oss å få en
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of teaching and administrative duties (up to 25 %), depending on the competence of the successful applicant and the needs of the department. The research fellow must take part in the Department’s PhD program
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statistical analysis. You must meet the requirements for admission to the faculty's Doctoral Programme in Social Science . Good oral and written presentation skills in English. The appointment is to be made in
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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
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on the admissions website admission to the doctoral programme in health sciences is a prerequisite for the position the final plan for organized research training must be approved and agreed no later than three
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well as candidates with a background in machine learning methods. The PhD programme will straddle the boundaries between the field of wave modelling and the general field of machine learning, and we will set up a team
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for this position. Applicants should be proficient in R, Python, or equivalent statistical software. Some background knowledge in either (computational) Bayesian methods, or statistical learning for molecular data