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platforms (specifically Microsoft Power Automate/Cloud Flow and SharePoint) and the ability to integrate Artificial Intelligence models (e.g., Gemini, Machine Learning APIs) to process unstructured data and
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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tools to support current business processes. Gain experience gathering software and systems requirements. Learn data modeling, data mapping, and data transformation. Train in developing reports utilizing
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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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, 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
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. Learning Objectives: By the end of this training/research experience, you will be able to: Explain the structure and functional organization of plant, insect, and/or fungal genomes and describe how genomic
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to: Learning about aircraft systems engineering and systems analysis to support integrated design and performance assessment. Participating in aircraft design trade studies with a focus on propulsion–airframe