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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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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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-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 calibration techniques recently
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