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Participation Program offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and temporal scales, from the genome to the continent
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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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gain hands-on experience and foundational knowledge in: USDA plant germplasm collections Plant phenomics and genomics Analytical chemistry techniques Plant propagation and tissue culture techniques
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physiology, molecular biology and pathology research projects. Trainings will include, but are not limited to, grapevine tissue culture, genetic transformation and genome editing, DNA/RNA/protein extractions
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diverse epidemiologic, clinical, laboratory, genomic, environmental, event-based, and open-source information; explore approaches to rapidly synthesize scientific and public health information during
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, overlap, and opportunities for integration; explore methods for measuring development and performance; analyze how clinical, laboratory, genomic, environmental, event-based, and other surveillance
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related to root system performance and function. Learn about transcriptomics, qPCR, sequencing, and related laboratory techniques by assisting in genomic and molecular research activities connecting
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with cell culture skills (human or animal models) with an emphasis on organoid and stem cell biology Analytical and molecular techniques (i.e. qPCR, ELISA, multiplex genomic and proteomic assays
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to reduce crop losses and enhance sustainability. In alignment with ARS National Program 301, we aim to leverage plant genetic diversity and genomics to develop improved germplasm and breeding tools
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interdisciplinary research teams on quantitative analyses of complex genomic datasets; Learn to use remote, high powered computer clusters to process large datasets. Mentor: The mentor for this opportunity is Adam