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application to field studies and other phenomic data for biological discovery and breeding predictions. Responsibilities: -Under general supervision, is responsible for conducting research into phenomics
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-truth data collection, and operating, testing, validating, and refining crop models to improve prediction accuracy. The candidate will also contribute to the development and application of machine
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time, building and validating ML/AI-based prediction models, creating field zones tied to management actions supported by farm economics, and developing scaling-up solutions for different management
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datasets. •Knowledge of genomic selection, genomic prediction, GWAS, quantitative genetics, and statistical learning for crop improvement. •Experience integrating multi-omics datasets (genomics
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pathogen genomics of a project building predictive tools for cotton soil-borne disease management. The work is supported through a cooperative agreement with USDA-ARS and is conducted in collaboration with