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Field
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social science research projects preferred. Experience with Bayesian estimation, machine learning, natural language processing, cloud-based computing, and/or Artificial Intelligence strongly preferred
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of ABM accuracy (e.g., Gaussian process emulation, Bayesian calibration methods, etc.). B4. Knowledge of epidemiological, statistical and microsimulation methods, and their strengths and weaknesses
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:10.1101/2023.11.14.566863. Developing and applying hierarchical Bayesian models to cognitive processes (available as IPhD) Supervisor: Dr Martin Lages MSc choice: MSc Research Methods of Psychological
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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
to Bayesian methods for complex innovative clinical trial design, adaptive designs, pragmatic trial design, Bayesian computation, prior elicitation, and Bayesian regression modeling with survival, longitudinal
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to academic leadership. Renowned for his work in Big Data and healthcare innovation, Dr. Madigan has authored over 200 publications covering topics such as Bayesian statistics, text mining, and probabilistic
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networks, Bayesian inference, computational neuroscience, mathematics.
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strengthen its actuarial science and statistics group, to support its ambitions to enhance and expand its expertise in these areas. Our staff work in various fields, such Bayesian statistics, applied
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modern clinical trial design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in
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be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high