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promoting U.S. agricultural health, regulating genetically engineered organisms, administering the Animal Welfare Act and carrying out wildlife damage management activities. These efforts support the overall
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, including data preprocessing, feature engineering, and preparation of datasets for robust model training and validation. Experience with computational and statistical analysis in Python and R. Familiarity
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access to resources and experiences that promote economic, ecological, and social vitality; connecting people to the land and one another; and delivering world-class science, technology and land management
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symbiont technology, specifically the development of transgenic galls that serve as biological platforms to produce and deliver therapeutic or beneficial products into host plants. This appointment provides
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. agricultural health, regulating genetically engineered organisms, administering the Animal Welfare Act and carrying out wildlife damage management activities. These efforts support the overall mission of USDA
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(e.g., command-line interface, scripting in R/Python, use of common bioinformatics software). Strong foundation in experimental design, advanced statistical analysis, and scientific writing. Proven
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, Master's Degree, or Doctoral Degree received within the last 60 months or anticipated to be received by 12/31/2026 11:59:00 PM. Discipline(s): Chemistry and Materials Sciences (12 ) Engineering (4 ) Life
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, and molecular breeding. Key learning activities include: Designing and conducting experiments in plant tissue culture, regeneration, genetic engineering, and genome editing of sugarcane. Developing
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statistical software such as SAS or R. Experience in preparation of manuscripts for publication in peer-reviewed journals. Team player, with the ability to collaborate effectively in a multi-cultural, multi
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stakeholders. To accomplish this, the participant will collaborate in a research team that includes chemist, textile engineer, and material scientist. When completed, the participant will have a thorough