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software engineering, Bayesian modeling and approaches to data analysis. Key Responsibilities: Preprocessing and data scientific approaches to analyzing human behavioral data Computational model development
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
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randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
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principles and analytic methods relevant in health services research Advanced knowledge of statistical computing and/or Bayesian inference Advanced programming skills in a common statistical software package
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute