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mathematics, statistics, or machine learning, or a closely related discipline • OR near to completion of a PhD • Expert knowledge of Bayesian computation and deep learning methods • Excellent
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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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Gravitational-Wave Astronomy Using Artificial Intelligence, to work on computational Bayesian inference methods and their astrophysical applications. Southampton's School of Mathematical Sciences is home to a
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation awareness and ecological interface design
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, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation awareness and ecological interface design
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to astrophysical flows (e.g., inversion methods, Bayesian/statistical inference, uncertainty quantification) Strong programming and data-analysis competence; ability to produce reproducible workflows. Experience
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Singlet and Triplet Excited States, Nature 2022, 609, 502–506. ・Bayesian Molecular Optimization for Accelerating Reverse Intersystem Crossing, Chem. Sci. 2025, 16, 9303–9310. (Scope of change) No change
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experience of Bayesian adaptive trial platform designs. relevant to public health and clinical research Strong statistical programming skills, with expertise in at least two of the software packages SAS, R