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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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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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2025.The position is part of the project "Validating fatty acid synthesis enzymes as targets for antibiotics against Pseudomonas aeruginosa and other Gram-negative bacteria”, financed by the Research
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resources policy, the University of South-Eastern Norway targets a balanced gender composition and aims to recruit persons from diverse backgrounds. The University contributes to the Inkluderingsdugnaden (a
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human resources policy, the University of South-Eastern Norway targets a balanced gender composition and aims to recruit persons from diverse backgrounds. The University contributes
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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fellow to be part of the HVL Robotics Research group. The PhD project will target semi-autonomous teleoperation where human inputs and autonomous control commands should merge seamlessly. Teleoperation
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) Documentation of English proficiency Contact information to 2-3 references Academic works, up to five. The doctoral thesis is regarded as one work In order to achieve the targeted use of the postdoctoral position