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Field
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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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infinite-dimensional (e.g., space-dependent) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive
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for inference, yet differs from standard Bayesian approaches through its information-theoretic foundation. The MML87 approximation achieves computational tractability while remaining virtually identical to Strict
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
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the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
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-ray Magnetic Circular Dichroism (XMCD), X-ray imaging or resonant magnetic scattering. Demonstrated ML experience (e.g., dimensionality reduction, spectral unmixing, Bayesian inference, or physics
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, United States of America [map ] Subject Areas: Bayesian inference; inverse problems Appl Deadline: 2025/12/31 11:59PM (posted 2025/10/09, listed until 2026/04/09) Position Description: Apply Position Description
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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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to the development of Bayesian inference frameworks that use GATES. What will you be doing? The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling