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predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications, and mortality. Gain experience analyzing administrative
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of science-based data collection activities (qualitative and quantitative) and data analysis. Strong science communication skills, both written and oral. Experience in logic modeling, strategic planning
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performance. Through this experience, you will gain hands-on exposure to analytical instrumentation, quantitative analysis, and modern predictive modeling techniques. Learning Objectives: Under the guidance
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data collection with quantitative statistical and modeling analyses and are collaborations with natural resource managers, veterinarians, and academics. You will learn to develop and conduct research
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and
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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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modeling techniques to identify patterns, trends, and emerging public health concerns. Design and evaluation of interactive data visualizations and dashboards to communicate scientific findings. Best
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, COMPASS-31 measures, etc. The project may include descriptive analysis, multivariable modeling, and integration of findings across epidemiologic datasets to improve understanding of infection-associated