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position We welcome applicants with a strong background in machine learning, causal inference, and statistics, who are eager to contribute to cutting-edge research at the intersection of these fields
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can have a disease modifying effect among persons who have already developed MS. The project will apply advanced methods on causal inference in observational studies. The work package is led by
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already developed MS. The project will apply advanced methods on causal inference in observational studies. The work package is led by Assistant Professor Kjetil Lauvland Bjørnevik at Harvard T.H Chan
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of Physics and Technology, Mathematics and Statistics, and Computer Science. More about the position We welcome applicants with a strong background in machine learning, causal inference, and statistics, who
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); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is
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Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. OCBE has collaborations with leading biomedical research groups in Norway and internationally. OCBE
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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implement a framework to infer anisotropic viscosity from both ice and mantle textures in a numerical flow model. This will open new avenues for understanding solid earth and cryosphere dynamics, and their
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. Integrate hydraulic-hydrologic modeling and surrogate models (e.g., Bayesian Networks) to simulate stormwater behavior under future scenarios. Apply optimization techniques to design and evaluate nature-based