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scientific programming (Python) and deep learning frameworks. Fluent oral and written communication skills in English. Desired qualifications: Expertise in broader topics in computer science, information
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learning and scientific profile relevant to the project described above. Proficiency in scientific programming (Python) and deep learning frameworks. Fluent oral and written communication skills in English
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. The plan is for the candidate tofocus on Bayesian modelling in close collabora-tion with researchers at the HISP centre that work on complementary deep learning approaches to disease modelling as
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at the HISP centre that work on complementary deep learning approaches to disease modelling as well as on development of plat-forms for running and evaluating prediction models. The PhD candidate will develop
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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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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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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