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4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
Stig Brøndbo 17th June 2025 Languages English English English Faculty of Science and Technology 4 PhD Fellows in Deep Learning at Visual Intelligence Research Centre and UiT Machine Learning Group
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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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knowledge in Python and/or R programming, and familiarity with deep learning packages Good oral and written presentation skills in English PLEASE NOTE: For detailed information about what the application must
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hypothesis that deep narrative structures in the datasets used to train generative AI models are replicated and perhaps exaggeratedin the output of generative AI, and that this could lead to culturally
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other postdocs, two PhDs, a research advisor, a data management expertand a research assistant. The project will explore and test the hypothesis that deep narrative structures in the datasets used
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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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-time fault prediction. ML models, such as deep learning, reinforcement learning, and ensemble techniques, can analyze large-scale operational datasets from hydroelectric power plants, identifying
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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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or Machine Learning). The Master’s thesis must be included in the application. Ideal Candidate: Demonstrates experience or strong interest in modelling, programming, systems thinking, and qualitative
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, Mathematics (Operations research) or Computer Science or Machine Learning). The Master’s thesis must be included in the application. Ideal Candidate: Demonstrates experience or strong interest in modelling