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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
-9-mediated genome editing and RNAi, heterologous protein expression, real-time qRT-PCR, and plant transformation using species such as Arabidopsis, rice, tobacco, sorghum, corn and wheat
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, real time PCR, protein expression, tissue culture, construction of recombinant virus, Western blotting, light and fluorescence microscopy, and flow cytometry. You may also have opportunities to use our
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Objectives: Under mentor guidance, you will develop experience in host–pathogen genomics by integrating crop and pathogen variation, gene expression, functional annotations, protein information, and disease
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or be completed by December 31, 2026. Eligible Veterans will have earned their Ph.D. within 10 years of application. Preferred Skills: Experience with some aspect of challenges on military installations
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through established and newly developed computational pipelines to identify patterns of gene expression, cell populations, and immune processes; and b) evaluate additional clinical, blood, and tissue sample
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addressing agricultural challenges related to water scarcity and crop production in California’s San Joaquin Valley. The region faces increasing constraints in water availability due to climate variability
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of cultivated and wild through multifaceted, modern techniques (genomic, expression analyses, proteomics, metabalomics, etc). This project aims to use these techniques to understand stress response and resistance
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, PCR/qPCR, gene cloning and expression analysis, lab, greenhouse and field studies of microbes, plants and facilities. Project assignments meet USDA-ARS's mission to find solutions to agricultural
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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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& simulation Bayesian networks & uncertainty quantification ML frameworks (PyTorch, TensorFlow, Hugging Face, OpenCV) & Python Automated pipelines for multi-modal data Continuous, bi-directional data flow