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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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The qualified candidate should be currently pursuing or have received an associate's, bachelor's, master's, or doctoral degree (including DVM, PhD, and MD) in the one of the relevant fields. Degree must have been
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of large population-based and healthcare databases within Division of Diabetes Translation's Chronic Kidney Disease (CKD) Initiative. Under the guidance of experienced CDC scientists, you will participate in
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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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surveillance initiatives. Laboratory data-management skills: You will learn to operate various instrument software and a laboratory management database for analytical result review, laboratory information
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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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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
sequence databases, analysis of gene function via CRISPR/Cas-9-mediated genome editing and RNAi, heterologous protein expression, real-time qRT-PCR, and plant transformation using species such as Arabidopsis
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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