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part of and gain training in a collaborative, multi-disciplinary research program incorporating chemical, biochemical, and engineering analyses aimed at development of novel biobased marketable goods
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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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, community service, research, and technology development, guided by its Strategic Principles. The mission encompasses patient care, education, and research to enhance societal contributions nationally and
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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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datasets to advance transporter discovery and engineering for domestic CMM recovery. CMMs, including cobalt, nickel, rare earth elements, zinc, manganese, and copper, are essential for modern technologies
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virus neutralization assays and/or ELISAs; evaluation of vaccine potency and efficacy in swine and cattle; and use of molecular and cellular techniques such as recombinant DNA technology, DNA sequencing
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transporter discovery and engineering for domestic CMM recovery. CMMs, including cobalt, nickel, rare earth elements, zinc, manganese, and copper, are essential for modern technologies and clean energy
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need. Applications of the technology advances are operational monitoring and scientific studies of how the human modification of landscapes affects water quantity, water quality, and sediment transport
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Details Title Postdoctoral Research Fellow School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Bioengineering Position Description The Walsh Lab at the Harvard
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, Computational Biology, Computer Science, Biomedical Engineering, or a related quantitative discipline Strong background in machine learning/deep learning (e.g., CNNs, transformers, vision models) Experience