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Requirements Strong computing and strong background/expertise in clustered data, survival data, causal inference or measurement error are desired. Strong written communications Additional Information: Per
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approaches may include—but are not limited to—causal inference, spatial analysis, computational text analysis, and archival or ethnographic research. Postdoctoral candidates with relevant research interests
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with expertise in causal inference, econometrics, experimental design, industrial organization, or applied microeconomics. Experience with field experiments, quasi-experimental methods, and/or structural
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to high-dimensional and multi-modal biomedical data. Causal Inference, Fairness, and Trustworthy AI in real-world healthcare applications. Our group actively collaborates with large national and
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transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches to high-dimensional and multi-modal biomedical data. Causal Inference, Fairness, and Trustworthy AI
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Transparency, and Societal Impact. Candidates should bring expertise in areas such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis
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data Clear scientific writing and communication; a track record of publications Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR
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data Clear scientific writing and communication; a track record of publications Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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research tasks and projects, making use of selected methodologies (longitudinal designs, moderation and mediation, causal inference), library research (Pubmed searches, systemic review methods), and