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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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departments in Oslo and several research groups at the University of Oslo. The main goal is to develop tools and knowledge for precision medicine in psychiatry, building on advanced statistical methods
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a single method for anisotropic flow modelling for both ice and olivine, by mapping CPO parameters directly to anisotropic viscosity parameters. This technique should reduce the computation complexity
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of the employment period. Experience in experimental and computational mass spectrometry methods is an advantage. Experience in the preparation of bulk and single-cell BCR libraries is an advantage. Molecular biology
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-event models and causal inference; epidemiology of lifestyle, occupation, chronic and infectious disease; methods for high-dimensional models and data integration (especially in molecular medicine
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years. There is a 10 % component of the position which is devoted to teaching and administrative duties. UiO/ Anders Lien via Unsplash UiO/ Anders Lien Qualification requirements A PhD degree in
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the field of economics of innovation and the economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered an advantage: applied microeconometrics and
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with and/or a well-described interest in the field of economics of innovation and the economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered
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with and/or a well-described interest in the field of economics of innovation and the economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered
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assimilation to calibrate the coupled CLM-FATES model using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model