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aerial vehicle (UAV) imagery collection and processing, deep learning methods, and rangeland vegetation communities in Oregon and Idaho as part of an interdisciplinary team including researchers in plant
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environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster analysis, to characterize populations with diabetes and
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
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genetic, genomic, and phenotypic datasets, to support research and crop improvement. Basic and applied research is also conducted within this project. You will use methods in computational biology to
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for Grain and Animal Health Research in Manhattan, KS. Our mission is to develop economical, effective, and ecologically sound methods for managing insect pests of grain and processed commodities, improve
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associated mechanisms. The primary focus of the fellowship is to gain experience in applying the scientific method to field trials and lab assays, collecting plant samples and data, and maintaining
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are to enhance barley and oat productivity, quality, and stress resilience by combing traditional, molecular, and genomics methods. You will collaborate with other scientists in the unit and other institutes
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industrial desert crop to farmers in the south western US as an alternative to high water requiring crops such as as cotton and alfalfa. Current methods will be optimized and new methods may be invented and
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phenotyping methods to assess susceptibility and evaluate established or newly developed screening approaches. This also includes developing and evaluating preharvest and postharvest treatments aimed
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their research. Additional funds are available for supplies and travel essential for the fellow's research. Research Project: Under the guidance of a mentor, you will apply computational methods for identifying