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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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predictive control, optimization-based decision frameworks, and data-driven performance modelling. The overall goal is to develop computational methods that enable efficient and intelligent operation of wind
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for developing reliable predictive models and ensuring safe and robust component design. This position is part of the CastAl project, which aims to identify the mechanisms governing stochastic fracture in HPDC
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long-term goal of our group is to develop a self-consistent model of FGFs and other lightning-generated high-energy radiation, then using radio and optical predictions from this model to compare against
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].. [1] Salomonsen, C. "A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [18F]FDG PET imaging. " EJNMMI Research, 2026. [2] Thomas, S
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and increased uncertainty in life and non-life insurance modelling. data-driven prediction of insurance premiums and associated quantification of uncertainty. Qualifications and personal qualities
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., probability, analysis), eager to conduct cutting edge research in the field of uncertainty quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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the University of Oslo. Place of work is the Department of Mathematics at Blindern, Oslo. Ocean waves follow complex patterns influenced by wind, currents, and the shape of the seafloor. Predicting