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, starting date, and project-specific qualification requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian inference and foundation models Employment: University
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populations wildlife diseases and conservation network analysis of disease spread phylodynamics model-based statistical inference using Bayesian approaches vector biology, ecology and control life-history trade
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flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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ranging from statistical fits to the data to mechanistic descriptions of shock-state evolution and hydrogen transport. Mathematical methods will include multivariate regression and Bayesian parameter