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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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inference, analysis of high-dimensional and -omics data, Bayesian methods, and clinical trials, with active collaborations in cancer, aging, HIV, and the analysis of large-scale health data. The School
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of statistical analyses, in particular: Exploratory and confirmatory factor analysis, Multilevel analyses (including latent class analysis), Time series analysis, Bayesian inference methods, Regression techniques
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datasets. Apply sensitivity analysis, parameter subset selection, and Bayesian inference to improve model identifiability and predictive capability. Implement computational pipelines in Python, MATLAB, and
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service. We welcome applicants from all areas of statistics. Preference will be given to candidates whose research interests overlap with the existing faculty, particularly causal inference, high
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underlying spatial data, high-dimensional and big data (e.g., data from wearable devices, electronic health records), Bayesian statistics, and learning algorithms as novel data analytical tools in applications
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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 hours ago
are particularly interested in scholars who advance methodological frontiers, such as causal inference, complex systems modeling, implementation science, longitudinal or big-data analytics, community-engaged methods
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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and