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Field
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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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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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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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, United States of America 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 The Department
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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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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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-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
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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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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 37 minutes ago
modalities Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response Implementing machine learning and statistical genetics