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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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within the past five years, or anticipated to be received by 5/31/2026. Preferred skills: Experience in computer science, bioinformatics, or computational biology Experience in machine learning and
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Eligibility Requirements Citizenship: LPR or U.S. Citizen Degree: Master's Degree or Doctoral Degree. Discipline(s): Computer, Information, and Data Sciences (7 ) Engineering (4 ) Environmental and Marine
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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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, Information, and Data Sciences (2 ) Environmental and Marine Sciences (7 ) Life Health and Medical Sciences (1 ) Science & Engineering-related (1 ) Veteran Status: Veterans Preference, degree received
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or currently pursuing. Discipline(s): Chemistry and Materials Sciences (11 ) Computer, Information, and Data Sciences (9 ) Earth and Geosciences (2 ) Engineering (2 ) Environmental and Marine Sciences (6
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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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Citizenship: U.S. Citizen Only Degree: Doctoral Degree. Discipline(s): Computer, Information, and Data Sciences (1 ) Earth and Geosciences (21 ) Environmental and Marine Sciences (14 ) Life Health and
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findings will be encouraged and supported. Learning Objectives: The fellow will have the opportunity to gain or expand skillsets over a range of computational techniques needed for modern agricultural
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): Chemistry and Materials Sciences (11 ) Computer, Information, and Data Sciences (9 ) Earth and Geosciences (2 ) Engineering (2 ) Environmental and Marine Sciences (6 ) Life Health and Medical