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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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training, travel to communicate findings, and professional networking will also be available. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) plan, execute
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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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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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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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surrounding areas to inform the PHAs. Network and gain communication experience through community outreach and coordination meetings with partners and key stakeholders. Learn about priority hazards and evolving
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through participation in professional meetings and trainings and networking with experts. Mentor(s): The mentor for this opportunity is Linda Hansen ([email protected] ). If you have questions about the nature
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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