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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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on increasing variability and extreme events in watersheds. Expand knowledge of process-based modeling approaches to assess relationships between forest species composition, biomass, and water yield Develop
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farming resulting in more nutrient-dense animal sourced protein products. Learning Objectives: The fellow will gain experience in planning and conducting data collection, remote sensing, geospatial modeling
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to monitor evapotranspiration (ET) and other surface fluxes at sub-field scales using both in-situ and remotely sensed data. Apply remote sensing models to estimate ET and surface fluxes using high-resolution
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gaps regarding reforestation under increasing disturbance Advance skills for modeling silviculture and genetics treatment outcomes Gain advanced data analysis skills in the data management, analysis, and
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multidisciplinary skills ranging from honey bee embryonic cell line research to large data analysis and modeling given the nature of the broad organismal to landscape level study. The fellow will also gain
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driving biology in non-model insect pests Expanding skills and practical knowledge of experimental design and data analysis Refining micro- and nano-injection systems for specific developmental stages