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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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research have been done to understand the physics of salt precipitation, but little is known on how geological heterogeneities at various scales affect the extent of salt formation. The project targets
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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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scales affect the extent of salt formation. The project targets modeling formation dry-out and salt precipitation during CO2 storage. The candidate will work on developing numerical models for salt