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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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. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
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analyses, including demographic inference, selection scans, and gene-environment and gene-phenotype association studies. • Plan and conduct fieldwork to collect plant material across Arctic locations, and
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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sciences or medicine, with a proven track record of cardiac research Previous experience with molecular cardiology, viral transduction, cell transfections, animal models, immunoblotting, qPCR, cardiomyocyte
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well as having a strong track record in relation to computational efficiency, which is fundamental in real time processing of streaming data. The main purpose of a postdoctoral fellowship is to provide
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. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance and recovery. It does this by allowing
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spoken English) . It is preferable that the candidate has (and can document): a strong academic track record experience in collection-based research (both physical and/or digital) teamwork and networking
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MIMICS+ module for soil carbon decomposition. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance