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fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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, they will have prior knowledge of infectious disease modelling, Bayesian inference methods and optimisation methods. They will have a developing research profile, with a demonstrated ability to publish
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Candidate We seek an ambitious researcher with a strong foundation in Bayesian theory, machine learning, and mathematical modelling. You should be confident in software development and capable of passing a
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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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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond