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carbon, water and energy states. The successful applicant will specifically support carbon and water cycle science, applications and process model innovations using CARDAMOM-based Bayesian inference
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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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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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experience) in epidemiology, mathematical modelling, or a closely related quantitative discipline. Strong skills in statistical inference and coding in R. Experience analysing epidemiological or infectious
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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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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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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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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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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