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. agricultural products. Learning Objectives: The participant will have the opportunity to learn about current methods for creating new catalytic materials and reactions to produce biofuels and chemicals. Hands
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to join this development team as a fellow and learn to create, evaluate, and validate rapid, accurate, and sensitive diagnostic methods for detecting disease pathogens. The fellow will have an opportunity
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extensive training in cell-based methods relevant to evaluating anticancer properties of natural products. Collaborating as part of a multidisciplinary team, the participant will gain experience in: Mammalian
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necessary to become credentialed as a Principal Investigator Applying a broad range of statistical and machine learning methods to human performance data collected in real-world settings Developing
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characterize labeled/unlabeled compounds. The fellow will collaborate with the team members on the development of high throughput analytical methods for target compounds and investigation of the fate of target
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computational methods, will be used to identify and validate promising candidates. Further design and engineering efforts will be utilized to optimize and port successful sensors into sensing platforms being
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for principles, methods, and procedures and selects the most appropriate for research goals and fiscal resources. Maintains official laboratory notebooks, recording methods and procedures used, and any procedural
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profit and, ultimately, pear industry viability. There are currently few effective control strategies for this disease. This project will develop validated quantification methods, pathogen resources, and
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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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use a range of spatial and analytical software for data analysis to include ArcGIS, Kaleidoscope Pro, and R. Other activities will include entering data, transferring acoustic files, data management