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regulatory environment that involves complex public health, biosafety, biosecurity, and laboratory oversight issues. Learning Objectives: Learning opportunities may include, but are not limited
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(ML), to help solve complex agricultural problems that also depend on collaboration across scientific disciplines and geographic locations. In addition, many of these technologies rely on the synthesis
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organizing and completing complex tasks. Proficiency in Office 365 Suite of Products: Outlook; TEAMS; SharePoint Ability to use Microsoft Teams for communication Experience with website design and user
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(RVFV). With mentor guidance, you will plan and execute complex experiments to study viral replication, pathogenesis, and transmission dynamics. Using a multidisciplinary approach, you will use or enhance
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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research priorities. Additionally, you will learn to synthesize complex scientific findings into technical evaluations, author peer-reviewed manuscripts, and develop empirical data visualizations
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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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, scientific, and regulatory steps required to translate in vitro molecular hits into small animal preclinical evaluation models. Processing and integrating complex transcriptomic, proteomic, and image-based
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development Ability to analyze complex datasets, identify trends, and effectively communicate findings to technical and nontechnical audiences Interest in applying data science and technology to public health
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understanding of micro- and nanoplastics (MNPs) as well as risk assessment. You will gain experience engaging with complex scientific information from a variety of disciplines related to MNPS, as well as data