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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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, and statisticians—on projects that bridge methods, policy, and practice. This role will involve intensive analysis of claims data and related large administrative datasets to support policy-relevant
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large administrative or claims data (e.g., Medicaid, commercial claims, or similar) Clear scientific writing and communication; a track record of publications Special Instructions Please include: · CV
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executing empirical studies of pharmaceutical competition, pricing, and regulation. The fellow will apply advanced econometric methods to administrative and other large-scale datasets—particularly claims data
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command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation
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is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
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is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
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large administrative or claims data (e.g., Medicaid, commercial claims, or similar) Clear scientific writing and communication; a track record of publications Special Instructions Please include: · CV
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to translate ground-breaking technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too