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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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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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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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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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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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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