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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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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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thesis is regarded as one work. In order to achieve a targeted use of the postdoctoral position, and to strengthen the employee’s career development, a development plan must be available no later than
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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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human resources policy, the University of South-Eastern Norway targets a balanced gender composition and aims to recruit persons with a background as an immigrant. The research area for the position may
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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