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the guidance of a mentor, this opportunity will involve: developing and applying methods in computational biology and artificial intelligence to gather information about gene function in the legume family; using
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/s12230-021-09859-8. Learning Objectives: The postdoc will have the opportunity to learn advanced bacterial molecular genetics, biochemistry, next-generation sequencing methods and functional genomics
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micrometeorological and remotely sensed datasets, as well as the analysis of soil and plant samples. Advance experimental design techniques by developing, evaluating, and refining innovative measurement methods
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. This knowledge will contribute to the development of more efficient breeding methods, which will be applied to genetic data mining of the USDA National Plant Genetic ornamental germplasm collections and their
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of a mentor, the participant will use a range of phylogenetic methods (including Bayesian) to study how interspecies transmission, genomic reassortment, and farm production practices affect the evolution
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affect oyster phenotypes targeted for improvement. The fellow will also gain experience using advanced computational methods to develop tools that can accurately predict desirable phenotypes. With
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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applied identifier (e.g., a collar with a name tag, an RFID microchip, or a skin tattoo). Ideally, an intrinsic method of canine identification equivalent to a human fingerprint would exist. Throughout
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these parasites as well as developing rapid methods of parasite detection and drug-resistant parasite strains. This project seeks to compile the genomic sequence of Eimeria, which are protozoa that cause
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quality attributes and resistance to diseases and pests, as well as a tolerance to environmental stresses. This will be achieved through a combination of traditional breeding methods, molecular techniques