142 parallel-computing-numerical-methods-"Institute-of-Computer-Science" Fellowship positions at Zintellect
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(BDCL), within the Nutritional Biomarkers Branch (NBB), Division of Laboratory Sciences (DLS), National Center for Environmental Health (NCEH), develops and applies analytical methods for measuring
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the quantitation of water-soluble biomarkers (e.g., vitamins B1, B2, and B6, methylmalonic acid) in serum, plasma, or whole blood matrices. Analytical method development and validation: You may gain experience in
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organizational goals. Designing and implementing a public health program evaluation using quantitative and qualitative data analysis methods. Reporting evaluation results using data visualization techniques
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security threats. The CDC IPP regulates the importation of infectious biological materials that could cause disease in humans to prevent their introduction and spread into the U.S. The program ensures
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project topics of interest – visit: https://orise.orau.gov/eif/default.html Questions? Contact ORISE at [email protected] with the subject line: “EIF Program Question”. Application Deadline 10/2/2026
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their research. Additional funds are available for supplies and travel essential for the fellow's research. Research Project: Under the guidance of a mentor, you will apply computational methods for identifying
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of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.. During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects
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crop protection products and other agricultural inputs. Research integrates agricultural engineering, physical sciences, and computational approaches to advance the understanding of atomization, spray
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methods to apply to genomic resources, or to develop novel genome annotation methods to apply towards existing genomic resources. Developing and customizing pipelines for automation of data processing
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and