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this subgroup focuses on the development of rapid, field-deployable detection methods for specific foodborne pathogenic bacteria (e.g., Shiga toxin-producing E. coli, Salmonella, Listeria monocytogenes). Analysis
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also receive training in use of Excel spreadsheets, PowerPoint, Visio, and plotting and statistical analysis using various software platforms. Learning Objectives: Under the guidance of a mentor you will
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pathogens such as Alternaria, Cladosporium, Mucor, or Penicillium is a plus. Applied research skills — Strong understanding of applied research, including experimental design, data collection, data analysis
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variety of meteorological and snowpack sensors. Learn to document avalanche activity and environmental conditions using standardized protocols. Data Analysis and Modeling Train to process and analyze
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environmental conditions, preparation and processing of biological samples, and analysis of ecological data using statistical and modeling approaches in R. Emphasis will be placed on building your analytical
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pathology and fungal ecology; (b) microbiome analysis, bioinformatics, and computational modeling; (b) conduct rapid mycotoxin analysis; (c) prepare scientific communication, and manuscripts; (d) design field
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genomic data analysis to identify resistance alleles associated with insecticide and fumigant exposure. Learn how environmental and ecological variables influence insect population expansion and resistance
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collaborators as well as USDA-ARS scientists. The participant will have the opportunity to expand knowledge and expertise in experimental design in vitro and in vivo research data analysis data interpretation
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management, with experience in statistical analysis and process-based modelling. A person with training in database development and management, as well as experience and interest in modeling soil carbon
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
involving physiological, biochemical, and molecular experiments and the use of numerous techniques such as RNA-seq, data mining of DNA and protein sequence databases, analysis of gene function via CRISPR/Cas