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biology Experience in machine learning and artificial intelligence Experience with genetics and genomic data Experience with large, diverse datasets and data mining approaches Proficiency in Linux and
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about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine
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to viral sequence data. Exposure to machine learning or artificial intelligence methods, particularly as applied to genomics or infectious disease data. Experience in next-generation sequencing (NGS) data
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skills: Experience processing and analyzing diverse geospatial environmental data products. Experience developing, testing, and refining machine learning models. Experience developing HPC workflows
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in Biology, Biochemistry, Cellular and Molecular Biology, Neuroscience, or Veterinary Science, particularly those considering a future PhD, DVM or MD degree. Learning Objectives: Under the guidance
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and Graphics Design (6 ) Computer, Information, and Data Sciences (17 ) Earth and Geosciences (21 ) Engineering (27 ) Environmental and Marine Sciences (14 ) Life Health and Medical Sciences
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computer programming. Learning to approach novel analytical problems with effective and appropriate solutions. Preparing briefings, technical reports, and manuscripts for publication in professional journals
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and experience with navigating science communications in a large government research institute and learn specific communication strategies that enable the success of WRAIR’s research mission and
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Kimberly Eligibility Requirements Citizenship: LPR or U.S. Citizen Degree: Bachelor's Degree, Master's Degree, or Doctoral Degree received within the last 55 month(s). Discipline(s): Computer, Information
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digestive diseases, primarily acidosis and ketosis. Under the guidance of a mentor, the participant will perform RNA extraction, quality control, RNA sequencing library preparation and data analysis. Learning