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Field
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discovery. Bayesian approaches provide a principled framework for modeling uncertainty by capturing posterior distributions over model parameters or predictions. Despite recent progress in approximate
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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emissions and exposures from observational data. -Work may include statistical or Bayesian inverse modeling, data assimilation, optimization, uncertainty quantification, numerical methods, and related
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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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(or higher) in Biostatistics or Statistics Experience: Prior teaching experience in Biostatistics or Statistics. Prior teaching experience in Biostatistics or Statistics. Experience in Bayesian inference
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attribution studies, including analyses during heatwaves. The role involves using and developing methods such as case-crossover designs, distributed lag non-linear models, Bayesian hierarchical models
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research interests in any area of statistics, including computational and applied statistics, biostatistics, modeling and analysis of complex data, Bayesian methods, statistical learning, or data science
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Bayesian meta-analyses. This position also provides opportunities to develop innovative statistical methods related to clinical trial design, variable selection in high-dimensional data, prediction
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian