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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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reliability-based design optimization and hierarchical Bayesian inversion. This specific PhD position focuses on the challenges within hierarchical Bayesian inference. Job description As the successful
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for Bayesian inference Documented experience with programming in either Python or R. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system Fluent
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at Stockholm University. We have a strong tradition in sampling but areas that we are growing in include, but are not limited to, Bayesian inference, the intersection of statistics and machine learning
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. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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international projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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clinical trials to assess its ability to measure hydration state. This project would use data from WearOptimo’s hydration sensor and develop novel Bayesian methods to model hydration state. How can hydration