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and any students who may be assisting with the research. Deal with problems that may affect the achievement of research objectives and deadlines. Promote equality and values diversity acting as a role
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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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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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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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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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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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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”, 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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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
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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