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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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-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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data, and boreholes. The candidate will revisit the current fault seal integrity algorithms and will contribute to improving the algo-rithms utilizing deep learning among other methods. A part of the
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Postdoctoral position in Natural Language Processing (NLP) is available at MediaFutures in the Language Technology Group (LTG) within the Section for Machine Learning at the Department of Informatics, University
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missions). Publication and support in proposal writing activities. Bring your own twist. Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency