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AgriLife and make a difference in the world! Position Information: The successful candidate will contribute to developing and evaluating terrestrial and aquatic nitrogen dynamics and emission modeling, with
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AgriLife and make a difference in the world! Position Information This position provides leadership to the modeling and analysis of historical data on groundwater extraction and agricultural irrigation in
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specializing in hydrologic modeling, water quality, carbon dynamics, and CO2 and CH4 emission modeling. The successful candidate will contribute to developing and evaluating aquatic carbon dynamics and CO2 and
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ecosystem modeling; downloading and processing projected future climate data; designing field experiments; procuring necessary field and laboratory equipment and supplies; conducting experiments; and
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. Responsibilities: Implement AI approaches for crop sensing, growth analysis, and stress monitoring, with a strong focus on modeling and interpreting plant-environment interactions for intelligent climate control
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organization, or summit coordination. Experience with modeling tools. Familiarity with poultry feed formulation software for research applications. Proficiency in laboratory techniques and use of analytical
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of drylands Knowledge of and experience with hydrological modeling, Knowledge of and experience using remote sensing. Knowledge of and experience in planning and executing field studies related to dryland
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applications, particularly in conjunction with process-based models and remote sensing for agricultural systems. A team-oriented individual who can collaborate effectively and contribute to the success
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statistical and geospatial software (R, ArcGIS, QGIS) and atmospheric dispersion modeling (AERMOD, WindTrax, CALPUFF) is strongly preferred. Ability to multi-task and work cooperatively with others. Why Work
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: Expertise in interpreting LC-MS, GC-MS, Raman spectroscopy, and NMR data. Experience in metabolic profiling and quantification of bioactive compounds in plants. Familiarity with computational modeling