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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
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have expertise in at least one of the following research areas: PDEs, numerical methods, optimization, functional analysis, or stochastic analysis Candidates without a master’s degree have until 1st
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or numerical methods in the context of environmental research is highly advantageous. Experience with the interpretation of field data is advantageous. Grade requirements: The norm is as follows: the average
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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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academic achievements in previous studies. Demonstrated knowledge of statistics and causal inference methods / econometrics, including good results in advanced courses. Experience with programming and
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have an excellent infrastructure covering chemical, structural, optical and electrical characterization methods. Part of the research will be conducted at the Micro- and Nanotechnology Laboratory , with
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or numerical methods in the context of environmental research is highly advantageous. Experience with the interpretation of field data is advantageous. Grade requirements: The norm is as follows: the average