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/or quantitative methods expertise. Experience with implementation science. Experience using statistical or qualitative software. Other Requirements: Full-time, one-year fellowship (renewable based
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will lead the execution of the experimental plan, work on the design of the data analysis methods, and perform the appropriate computational analyses. You will perform literature searches and use them
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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activities. Create scientific figures, model visualizations, and technical documentation. Contribute to methods development and reporting for peer-reviewed publications and conference submissions. Preferred
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of epidemiologic methods, observational study design, cancer outcomes research, population health, and health disparities research. Experience analyzing large secondary datasets, claims data, registry data, EHR data
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modeling and analysis. Ability to select, implement, diagnose, and adapt parameter-estimation or statistical-inference methods to suit the model, data structure, and scientific question. Experience with
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, bioassay development, bioanalytical methods operation. Candidates should demonstrate strong communication skills, a record of independent research productivity, and enthusiasm for working both independently
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repositories Research papers and theses Model-related websites Other linked sources discovered by the AI agents Map the generated metadata to established ontologies and Common Data Elements where possible. When
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degeneration. 2. Transposable elements in aging and degenerative disease The postdoctoral fellow will develop and apply cutting-edge genomics and proteomics approaches to investigate the mechanisms by which
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microscopy, optogenetics, or in vivo imaging techniques. Experience with electrophysiological recording methods, including patch-clamp techniques. Programming and data analysis experience using MATLAB, Python