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, clustering, or other multivariate methods is desirable. Familiarity with SEM and Raman spectroscopy/chemical imaging. Prior experience with cryo-SEM is desirable; project-specific training will be provided
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
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environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster analysis, to characterize populations with diabetes and
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., Medicare and Medicaid) to examine clinical and environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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interdisciplinary research teams on quantitative analyses of complex genomic datasets; Learn to use remote, high powered computer clusters to process large datasets. Mentor: The mentor for this opportunity is Adam