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) Department What will I be doing and why should I apply? As the selected candidate, you will engage with data scientists, and systems engineers in research projects with emphasis on various advanced modeling
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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transport, deposition, application system performance, and the measurement and modeling technologies that support these systems. Research Project: Under the guidance of a mentor, you will have the opportunity
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. This creates large uncertainties when modelling orchard water and nutrient fluxes as well as deep percolation below the root zone, critical topics in semi-arid, agriculturally productive regions such as
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on the development and evaluation of snowpack and hydrologic models used for avalanche hazard assessment. Applied research that informs avalanche forecasting centers, land managers, and community stakeholders. You
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collecting soil, plant, and water samples; monitoring soil moisture with advanced sensors; organizing and processing research data; and contributing to modeling efforts that examine water and nutrient dynamics
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predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications, and mortality. Gain experience analyzing administrative
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performance. Through this experience, you will gain hands-on exposure to analytical instrumentation, quantitative analysis, and modern predictive modeling techniques. Learning Objectives: Under the guidance
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to classify rangeland plant species, and (2) using transfer learning to adapt deep learning models for imagery analysis to varying UAV sensors and conditions. These techniques will allow you to identify and
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with these plants using in vitro and in vivo animal models. Animal tissue and cell culture models will be used to study the ability of compounds of interest to cross absorptive barriers as