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disease, including AI-powered tools and new statistical techniques that leverage large datasets, heavy computational capabilities, and/or a robust understanding of biological systems to provide unique
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Science Lab is seeking a highly-motivated postdoctoral scholar to lead a large-scale, community-based, clinical trial of behavioral treatment for childhood obesity. The Solutions Science Lab designs
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Postdoctoral Research Fellow with expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through innovative data-driven approaches in
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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experience working with large, complex data from varied sources. The candidate should have proficiency with statistical software, ideally Stata, strong written and interpersonal communication skills, and a
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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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, and dementia more generally. Data sources for our work in this area are large-scale electronic health record data, medical claims data, mortality registries, and epidemiological cohort studies
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genetics and biochemistry to work on projects related to precision diagnostics for diabetes. Research will be focused on generating large maps of variant effects using disease relevant assays and state
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population-level outcomes. Utilize advanced quantitative methods to analyze large healthcare datasets, including Medicare and Medicaid claims (MedPAR, Outpatient, Carrier, TAF). Develop reproducible code and
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, RedCap). Conduct field monitoring and troubleshoot real-time data collection challenges (e.g., technology, logistics, respondent recruitment). Data Management & Analysis Clean and manage large-scale