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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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related area, including meteorology, hydrometeorology, remote sensing, surface and atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https
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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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will I be doing? Under the guidance of a mentor, you will learn and gain hands-on experience to complement your education and support your academic and professional goals. This includes, but is not
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to model fungal population shifts and identify environmental or biological drivers of mycotoxin risk. Collaborating with plant pathologists, microbiologists, chemists, and data scientists to develop
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at the Department. Fellows will receive hands-on learning that provides an understanding of the mission, operations, and culture of the DOE. As a result, fellows will gain deep insight into the federal government's
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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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activities across the branch. You will learn about public health performance improvement, supporting PPIB projects with national partners, assessing the national voluntary accreditation program for public