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the application of machine learning/artificial intelligence (ML/AI) in environmental health. This project aligns with ATSDR's current strategic initiatives and will provide you with opportunities
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the shallow subsurface (<10 meters depth). Experience with soil moisture/salinity and sapflow sensors. Experience using neural networks and machine learning tools. Stipend $70,000.00 – $80,000.00 Yearly Point
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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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also receive training in use of Excel spreadsheets, PowerPoint, Visio, and plotting and statistical analysis using various software platforms. Learning Objectives: Under the guidance of a mentor you will
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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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are highly desirable. Familiarity with data science and machine learning applications for analytical chemistry, industrial/agricultural facilities, and techno-economic or life-cycle analysis is considered a
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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
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in a Unix environment and on high-performance computing equipment. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) apply methods in computational
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Description *Applications are reviewed on a rolling-basis. ARS Office/Lab and Location: A Postdoctoral Research opportunity is currently available in the Mycotoxin Prevention and Applied Microbiology (MPM