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design concepts, integrated data collection and analysis methods. The participant will have the opportunity to learn more in-depth statistical analysis techniques or molecular biology tools (bioinformatics
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of a mentor, the participant will be involved in microbial sequence data analyses using bioinformatic tools, identifying beneficial microbes from soil and plant rhizosphere, and compiling the research
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. The student will learn to utilize molecular techniques, microbial culturing, metabolomics, proteomics or bioinformatics/statistics skills. Mentor(s): The mentor for this opportunity is Shin-Yi Marzano
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perform unbiased and amplicon sequencing. process and interpret data sets generated through sequencing. prepare data for scientific presentation (i.e. abstracts, posters, presentations and papers). Mentor(s
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of ticks and animal hosts relevant to CCHFV transmission. Collect, process, and analyze samples from arthropods and/or vertebrate hosts. Conduct molecular and serological analyses to detect and characterize
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expedite research findings on understanding fundamental plant growth and developmental processes. The participant will gain advanced genetic and genomic knowledge on grapevine stress responses and enable
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sprayer operation parameters for a next-generation electric orchard sprayer to enhance spray deposition on crops and reduce drift. Under the guidance of a mentor, the research fellow will examine the
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process, contact NETLinfo@orau.org . After you have submitted an application in Zintellect, you may reach out to internship.program@netl.doe.gov to request to talk with the hosting researcher if you would
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project and analyzing the malt quality data to assess potential gains in the brewing and distilling process that can be attributed to barley variety or processing methods. Learning Objectives: Under
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to collect phenotypic data from malts produced from project and analyzing the malt quality data to assess potential gains in the brewing and distilling process that can be attributed to barley variety