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pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities
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development, institution review board protocol(s), and data collection Learning about new domain(s) relevant to the Cognition and Modeling Branch at Wright-Patterson AFB Reporting research updates in individual
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the plant family as a whole; and to identify correspondences among agronomically important genes across many crop and model species in this large plant family. After identifying corresponding genes, genomic
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the underlying impacts of stressors on bee health and analytical skills for understanding colony level dynamics and predicting mass colony loss events. Learning Objectives: The fellow will learn and apply
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, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning
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in vitro and in vivo infection models, flow cytometry, ELISA, western blots, and multiplex immunoassays. Our projects rely on successful partnerships with collaborators across academia and government
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of the U.S. national harvested wood products carbon model (WOODCARB II)”. US Forest Service has been using the WOODCARB II model to estimate and reporting of the U.S. annual harvested wood products (HPW
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional
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the mentor’s guidance, the participant will design and conduct experiments in the laboratory and the field and use data to test and validate predictive models. Interaction with stakeholders and research
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of genes and proteins as regulators of physiological or immunological traits. Learning Objectives: Under the guidance of the mentor, the candidate will gain experience in and learn to utilize a functional