49 machine-learning-modeling-"Linnaeus-University" Fellowship positions at Zintellect
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about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine
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modeling, economic evaluation, and surveillance to enhance analytical tools. Activities will focus on gaining hands-on experience and contributing to emergency preparedness and surveillance planning
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of microphysiological systems or organ on a chip model for viral agents. Research project emphasis is placed on determining virus growth and stimulation of an appropriate immune response that mimics what is observed in
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, or Arcata, California. Learning Objectives: Improve knowledge of issues and science surrounding forest restoration and hazardous fuels reduction Advance skills for modeling silvicultural treatments in
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compared to a conventional sprayer. Learning Objectives: The participant will have the opportunity to develop or enhance their experience with experimental design and modeling as well as develop scientific
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researchers engaged in a Defense Health Agency project on spatial orientation modeling and disorientation mitigation. The research fellow will participate in generating a report for the Spatial Orientation
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research-operational partnerships and learning about systems involving forest fuels and fire emissions modeling. They will gain experience with modeling, coding, and database management in support of a
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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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of innovative technology solutions. Assignments will require technical and economic research involving data analysis, regulatory analysis, and modeling. You will learn by assisting with producing this research
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that pose a threat to US agriculture and public health. Potential areas of research include genomic epidemiology, virus evolution, ecological surveillance, ecological modeling and modeling of viral