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a doctoral degree in one of the relevant fields (e.g., Plant Genetics and Breeding, Plant Biology, Genomics, Evolutionary Biology), or currently be pursuing the degree to be received by January 1
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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have the opportunity to learn to: Understand viral evolutionary biology and mechanisms of pathogenesis Evaluate host–pathogen interactions, immune modulation, and ruminant immunology Conduct
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collaborating with the mentor, you will investigate evolutionary changes in pathogen populations, assess the role of microbes and host physiology in disease resistance and susceptibility, and evaluate non
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scales, from the genome 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 AI and machine learning
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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interdisciplinary exposure to genomics, bioinformatics, agricultural science, evolutionary biology, and pest management research. Mentor(s): The mentor for this opportunity is Erin Scully ([email protected]
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required for discovering genetic (e.g., SNP, CNV) and epigenetic (e.g., DNA methylation) variations to support eco-evolutionary studies, QTL mapping, or other applications of genomic sequence variation
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relationships, methods for inferring gene families and for inferring evolutionary events from large collections of gene families; (b) conduct writing within scientific manuscripts. Mentor(s): The mentor