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the application of rock 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
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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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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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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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equipment budgets (e.g., computer, phone and subscription), subsidized canteen meals, comprehensive travel/health insurance policy, etc; Relocation assistance: accommodation, visas, complimentary Norwegian
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consistently ranked for its high quality of life and stunning access to nature. Numerous benefits: access to company cabin, sponsored social events, generous equipment budgets (e.g., computer, phone and
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engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and
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snow in local and regional climate models is poorly constrained, leading to uncertainties in estimating mass loss through sublimation and snow redistribution. The PhD candidate will develop and execute
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estimating mass loss through sublimation and snow redistribution. The PhD candidate will develop and execute a field campaign focused on observing the role of blowing snow on sublimation and redistribution
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engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and