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associated clinical outcomes. The fellow will be responsible for identifying computational approaches for data selection, processing, and predictions/inference. The expected outcome of the project is to
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designs employed in analytic observational epidemiology, their biases and limitations, and available approaches to adjust measurements to support valid causal inference. Growing peer network where sought
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extraction processes that systematically capture key bias information for adjustment in analysis and causal inference. Must have demonstrated competence with analytic tasks and ability to participate
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, certification, and/or registration. Additional Qualifications Working knowledge and experience with R, Stan, Nimble, or other relevant analytical software. Knowledgeable of Bayesian statistical methods, numerical
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). Explainable AI (XAI) for transparent and accountable decision-making. Causal Inference, Counterfactual Reasoning, and Reinforcement Learning with Human Feedback for dynamic AI-human collaboration. Oversee
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, and statistical methods required for the proposed work, including causal inference and relative risk analyses. We are seeking an experienced and motivated Research Scientist to contribute
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with large tech AI companies, Pharma AI/IT groups, and Academic collaborators. Build and optimize scalable training and inference pipelines for structured and unstructured biomedical data. Collaborate
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scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief networks; etc.); and interpreting, communicating to broad