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power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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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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. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology for analysis of complex polygenic traits and prediction tools for precision
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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