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for wheat, barley, oat, and rye. As part of a highly collaborative, multi-disciplinary team, the selected candidate will use his/her computational biology and machine learning background to help develop tools
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learning background to help develop tools to transform data query, access, display, and sharing, perform analyses, present results, and publish manuscripts. Learning Objectives: The participant will learn
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-enriched staples) on metabolic dysfunction and inflammation. Learning Objectives: Under the guidance of a mentor, the fellow will have the opportunities to: Develop hypothesis-driven studies to examine the
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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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Agrilus pest in Northern U.S.. Learning Objectives: Through participation in the research project, the participant will learn principles of classical biological control as well critical thinking skills and
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to a motivated postdoctoral fellow interested in learning and using contemporary functional genomics approaches (CRISPR, RNAi, transgenics, etc.) to characterize the functions of genes involved in
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, perform sample preparation, instrumental and data analysis, and write scientific per-reviewed manuscripts and reports. Learning Objectives: The participant will learn to conduct research in the food safety
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in the western US. Learning Objectives: The fellow will learn advanced laboratory procedures related to plant pathology, weed science, and biological control; gain an understanding of the purpose and
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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