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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
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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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probability, likelihoods and Bayesian analysis. We are also seeking individuals with a strong interest in public health. Key Responsibilities: Develop models that integrate different data types (e.g., serology
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(e.g., REDCap). Conducts complex statistical analyses on observational studies and clinical trials, applying techniques including regression models, multiple imputation, nonparametric methods, Bayesian
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Masters project Supervisors Login Recently added Development of a GIS-Based Model for Active Citizenry Street-Level Environment Recognition On Moving Resource-Constrained Devices Bayesian Generative AI (PhD
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, and visualization are preferred. Prior training in longitudinal data analysis, survival analysis, Bayesian methods, and joint modeling is highly desirable. Experience working with clinical or biomedical
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testing, propensity score methods, meta-analysis, Bayesian inference, and a wide range of regression models (linear, logistic, Poisson, negative binomial, lognormal, Cox, mixed-effects, GEE, penalized
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assessment models using approaches such as Bayesian networks and system dynamics, leveraging domain expertise and statistical tools (e.g., SPSS, Vensim) to model cyber-physical risk scenarios in the maritime
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit
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in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov