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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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healthcare services including ethical and practical challenges and opportunities for improving quality and safety. The study will also investigate how practitioners perceive new methods and international
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A4 poster of project description including reference list. The outline should present and discuss possible research questions, theory perspectives, material, a progress plan, and methods within
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a single method for anisotropic flow modelling for both ice and olivine, by mapping CPO parameters directly to anisotropic viscosity parameters. This technique should reduce the computation complexity
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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness
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departments in Oslo and several research groups at the University of Oslo. The main goal is to develop tools and knowledge for precision medicine in psychiatry, building on advanced statistical methods
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-event models and causal inference; epidemiology of lifestyle, occupation, chronic and infectious disease; methods for high-dimensional models and data integration (especially in molecular medicine
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of the employment period. Experience in experimental and computational mass spectrometry methods is an advantage. Experience in the preparation of bulk and single-cell BCR libraries is an advantage. Molecular biology
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the theories and methods intended to be used. The project proposal plays an important role in evaluating applicants and must demonstrate how the project will lead to the successful completion of a doctoral
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developing data analysis methods, so the PhD candidate will benefit from collaborations within the group. Roles and responsibilities of the PhD fellow The candidate’s specific research tasks will include