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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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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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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
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data collection with quantitative statistical and modeling analyses and are collaborations with natural resource managers, veterinarians, and academics. You will learn to develop and conduct research
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of science-based data collection activities (qualitative and quantitative) and data analysis. Strong science communication skills, both written and oral. Experience in logic modeling, strategic planning
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your skills in molecular biology, virology, cell culture, vector biology, and animal models, to understand what drives the arbovirus transmission cycle. Additionally, you will be provided opportunities
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health-related projects. Primary techniques and approaches will include establishing intestinal organoid in vitro models to test hypotheses relating to mechanisms response of intestinal epithelial cells in
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, transgenic/gene-targeted mouse models, and natural hosts (e.g., cattle, deer, and sheep). Note: Research will be carried out in BSL-2+ or BSL-3 laboratories. The fellowship activities involve utilizing
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scientists to characterize movement patterns of stored-product insects across U.S. agroecosystems and to identify invasion pathways. This includes modeling environmental variables that predict population
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data analysis, data visualization, data modeling, database design, and/or data curation Experience using Microsoft Power BI and Excel to analyze data and develop reports, dashboards, or other data