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Experience or interest in artificial intelligence (AI), machine learning, or AI-assisted workflow automation Experience with software testing, beta testing, user acceptance testing, or information system
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Must be a U.S. Citizen Preferred Skills: Experience with MATLAB Familiarity with emerging technologies including instrumentation, computer modeling & simulation (e.g. Matlab, CFD, and/or other), and the
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measurements and remote sensing data products, in a unified data processing workflow to recover models of subsurface water content variation. Learn about the use of physics-informed neural networks developed by
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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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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