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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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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
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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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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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and conducting climate model simulations and analysing large volumes of ESM simulations. Knowledge of reduced-order modelling and Bayesian inference is highly valued, and experience with climate
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, and lineage-specific dynamics. Assess congruence and robustness of phylogenetic reconstructions using Bayesian inference, parsimony, and tip-dating, and evaluate their impact on macroevolutionary
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learning, evidence synthesis in public health and statistical genetics and genomics. We are recognised for our strength in Bayesian inference applied to biomedicine and public health. The MRC Biostatistics
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-represented backgrounds. The objective of the research project is to perform Bayesian inversion to characterise the velocity field of 3D partial differential equations describing brain fluid and solute movement
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to address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse