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transcriptomic and other genomic analysis approaches Apply laboratory techniques, sample processing, and data interpretation in a research setting Mentor(s): The mentor for this opportunity is David Holthausen
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. coli, Salmonella, Listeria monocytogenes, Campylobacter). Analysis is typically complicated by the complex nature of food matrices and the frequent need to detect very low numbers of targeted pathogens
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at research conferences and seminars. Develop statistical and bioinformatic skills through analysis of experimental data and population genetic and genomic data from plant pathogens and beneficial microbes
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molecular breeding of value-added traits in oilseeds, primarily cotton, and analysis of lipids, DNA, RNA, and other metabolites produced by such plants. The research will combine experiments conducted in a
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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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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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instrumentation, quantitative analysis, and modern modeling approaches that help connect fiber properties to fabric performance. Learning Objectives: Under the guidance of a mentor, you will: Develop
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months. Preferred Skills: A background in computer and/or data science with some experience in machine learning, multivariate statistical analysis, artificial intelligence, or computer programming. Some
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diverse food commodities Build competency in BSL-2 lab practices, including safe handling of foodborne pathogens Gain experience in food quality analysis after the use of novel interventions Develop