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targeted metabolomic methodologies via LC/MS and GC/MS techniques. Collect and Curate post-analysis data, collaborating with bioinformatics scientists to identify biomarkers and metabolic mechanisms impacted
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specimen-based research and long-term curation. Bioinformatics and computational analyses will be supported by USDA’s SCINet high-performance computing infrastructure, which provides large-scale computing
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bioinformatics. Participant will collaborate with team members to think up novel approaches to fill such data gaps so as to help resource managers deal with wildlife health issues in a timely and cost-effective
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doctoral degree in the one of the relevant fields (e.g. Agricultural Sciences, Agriculture, Entomology, Bioinformatics, Computational Biology, Biology, etc.). Degree must have been received within the past
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, bioinformatics, protecting agricultural against invasive species, natural resources, remote sensing, advanced manufacturing, and the nexus of agriculture and space. Pursue knowledge in coordinating USDA’s research
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signaling. Learning Objectives: The participant will gain skills in bioinformatics, genetics, data analysis, statistics, and artificial intelligence-based methods for protein modelling. The participant will