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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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competitiveness and excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence. The SCINet/Big Data Research Participation
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. The participant will have the opportunity to learn and enhance skills in the following areas: Design and Implementation of Field Studies: The fellow will learn how to design and implement large-scale field studies
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laboratory techniques, including Sanger sequencing and Next Generation Sequencing, emphasizing precision and accuracy in data generation. The fellow will also learn to analyze sequencing data using statistical
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adapting methods from published literature. Through meticulous record-keeping, data tabulation, statistical analysis, and summarization using computer software, you will gain practical experience in managing
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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide
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or U.S. Citizen Degree: Doctoral Degree received within the last 12 months or anticipated to be received by 7/31/2026 11:59:00 PM. Discipline(s): Computer, Information, and Data Sciences (3