-
, computational, and immunological tools within an ABSL-2 laboratory environment. You will be mentored in research methodologies aligned with the USDA–ARS mission in animal health and infectious disease. You will
-
Participation Program offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and temporal scales, from the genome to the continent
-
., enhancers, promoters) in focal species. Customize bioinformatics pipelines for automated processing and analysis of short- and long-read sequencing data. Apply computational approaches to detect genetic and
-
interactions from every perspective—molecular, microbe, and natural host. For more information about the Agricultural Research Service (ARS) Research Participation Program, please visit the Program Website
-
, Computer Science, Systems Engineering, or a closely related field. A postgraduate is required to have earned their degree within 5 years of the appointment start date. A PhD is not required at the time of selection
-
well as groundwater geophysicists, hydrologists and economists. Participation with the SAWS unit will grant access to USDA physical and computational resources, including geophysical instrumentation, soil and plant
-
—from chickens to bison and everything in between. This opportunity is in the endemic swine viruses laboratory group of the Virus and Prion Research Unit, which advances a comprehensive research program
-
educational level and experience. The anticipated stipend range is $74,678.00 – $77,168.00 yearly. Citizenship Requirements: This opportunity is available to U.S. citizens only. ORISE Information: This program
-
research program that includes bioinformaticists, chemists, geneticists, and plant physiologists. Over the course of this opportunity, you will develop a thorough understanding of maize physiology, fungal
-
Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically