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. Please direct all questions about the position to Dr. Jessica Jaynes at jjaynes@fullerton.edu . Statistics at CSU Fullerton The statistics faculty research areas include Bayesian statistics, statistical
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and reduction Development and application of big data analytics for large X-ray data sets Application of Bayesian methods to X-ray data Combinatorial analysis of various data from complementary
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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Bayesian statistics, statistical computing, spatial statistics, experimental design, and survival analysis. Faculty are active in application areas of neuroscience, geology, biostatistics, psychometrics
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multiple statistical and modelling approaches, including Bayesian approaches. About the Department Ecology, Evolution, and Behavior (EEB) faculty teach undergraduate classes, advise graduate students, and
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increasingly important, but also more complex, due to rising demands on performance, precision, quality, and sustainability. Bayesian optimization (BO) - a special machine learning approach - represents a
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questions about the position to Dr. Jessica Jaynes at jjaynes@fullerton.edu. Statistics at CSU Fullerton The statistics faculty research areas include Bayesian statistics, statistical computing, spatial
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redefinition of behavioral features or pose challenges in their detection. The projects To address these challenges, we propose developing a Bayesian Program Synthesis (BPS) methodology for generating synthetic
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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple