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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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agreement between data and model, hence making easier the detection of inconsistencies between the two. Key Responsibilities: Research & Development - Conduct original research in robust Bayesian inference
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contributing to more trustworthy and robust inferences. In specific, the candidate will: Combine formal Bayesian theoretical connections with quantitative experiments to develop methods for quantifying
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a spatially explicit predictive model for Everglades vegetation dynamics in response to major drivers. The major objectives are to explore the distribution models that discriminate among prairie and
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc . The successful candidate will be developing Bayesian models for estimating
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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Physics and Number Theory. More broadly, the School also has research strengths in Bayesian and Monte Carlo Methods, Biomathematics, Biostatistics and Ecology, Computational Mathematics, Data Science
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interests in the areas of applied and pure mathematics, and statistics. In Statistics, the School has research strengths in Bayesian and Monte Carlo Methods, Biostatistics and Ecology, Combinatorics, Data
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an inclusive community that is respectful and fair for all. Find out more about the benefits of working at the University and what it is like to live and work in the Durham area on our Why Join Us