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climate. Observational work would include data from GRACE, SMAP, GPM or in-situ stations. Model diagnosis and analysis can include CLM, VIC, CLSM, and LIS frameworks including GLDAS and NLDAS. Model
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, data analysis, and the application of AI/ML techniques in agricultural research. You will also gain a holistic understanding of the entire research data lifecycle, from experimental design and data
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at the speed of commerce under real-world conditions. The participant will collaborate with a research team to explore best practices in experimental design, hypothesis testing, and data analysis for assessing
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of Agriculture (USDA), Animal Plant Health Inspection Service (APHIS), Veterinary Services, Center for Epidemiology & Animal Health (CEAH) Domestic Animal Health and Analysis Unit (DAHA) examines all aspects
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and incorporation into a clinical diagnostic lab under ISO-1705 procedures. The fellow will learn and contribute to the experimental design, data analysis and troubleshooting. The fellow will present
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offer the most robust trials of forest restoration outcomes available. The research fellow will participate in the analysis and write-up of two related long-term silvicultural field trials: Blacks
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. Research Project: Joining a team of landscape and fire ecologists to learn about and support geospatial analysis of fire mitigation implementation in the highly fire prone lands of Kona and Kohala on Hawaii
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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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. This fellowship offers a unique opportunity to be at the forefront of groundwater technology, gaining experience on projects ranging from groundwater sampling and geophysical imaging in the field, analysis
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of the relevant fields (e.g. Ecology, Forestry, Climatology, Geography). Degree must have been received within the past four years, or anticipated to be received by 9/30/2026. Preferred Skills: Data analysis and