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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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, which aims to characterize bile duct inflammation in order to identify targetable molecular pathways using a range of multiomic techniques. In-depth characterization of animal models of bile duct
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
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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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of novel cancer targets. This project funded by the Norwegian Cancer Society focuses on the mechanisms underlying chromatin interactions and gene deregulation in Sarcoma. To elucidate these mechanisms in
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mapping epigenetic aberrations that enable identification of novel cancer targets. This project funded by the Norwegian Cancer Society focuses on the mechanisms underlying chromatin interactions and gene
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. The project targets modeling formation dry-out and salt precipitation during CO2 storage. The candidate will work on developing numerical models for salt precipitation at pore- to continuum-scale, with special