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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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, 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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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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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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developing algorithms for RNA-seq and whole-genome data. Learning Objectives: Under the guidance of mentors, you will have the opportunity to learn to: (a) conduct research using swine infection models; and (b