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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
the use of natural products, either by discovering safer natural pesticides or by manipulating their production in plants by genetic engineering or other means. You will use plant molecular biology to
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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underlying genetic components, helping researchers better understand how plant genetics and fiber quality interact. You will engage in a collaborative, multidisciplinary research program spanning chemistry
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of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence. The mission of the USDA-ARS Corn Insects and Crop Genetics Research
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accelerating the improvement of pecan cultivars through the discovery of novel genetic variation, development of genomic-assisted management techniques, or other applications of genomic technology in pecan
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
. This fellowship aims to improve crop protection from pests through the use of natural products, either by discovering safer natural pesticides or by manipulating their production in plants by genetic engineering or
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of microbial genetics and physiology to optimize fermentation strategies through novel strain development and modification of bioprocessing parameters. Learning Objectives: You will gain experience in
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Salmonella microbiology (culture, isolation, characterization) Molecular characterization and genetic typing Whole-genome sequencing Transcriptomics Data analysis Data visualization Point of Contact Sara Beth
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The qualified candidate should be currently pursuing or have received a doctoral degree in the one of the relevant fields. Preferred skills: Experience in analyzing population genetics or genomics datasets and
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. The participant will have the opportunity to collaborate with a multidisciplinary team that is focused on identifying evaluating and manipulating the genetic, metabolic, and physiologic factors associated with