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USDA-ARS Fellowship in Geospatial Analysis and Mapping of Livestock Disease

ARS Office/Lab and LocationA research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Foreign Animal Disease Research Unit (FADRU)  located in Plum Island, New York.

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief scientific in-house research agency with a mission to find solutions to agricultural problems that affect Americans every day from field to table. ARS will deliver cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustain our nation’s agroecosystems and natural resources; and ensure the economic competitiveness and excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence.

Research Project: The project goal is to develop and apply new geospatial technologies, tools, and methods, including artificial intelligence (AI) and machine learning (ML), to help solve complex agricultural problems related to livestock disease and disease risk assessment. Project activities will include the synthesis, integration, and analysis of large, diverse datasets and application of geospatial analysis in a high-performance computing (HPC) environment. Under the mentor's direction, the candidate will perform spatial analyses, statistical computer coding, and serve as an active participant during meetings with project collaborators. 

This research opportunity will support the ARS Grand Challenge in Predictive Disease Ecology and the Foreign Animal Disease Research Unit (FADRU) at ARS. This fellowship offers a research opportunity to help solve agricultural problems related to livestock and animal diseases through the analysis and visualization of geographic data across a range of spatial and temporal scales and facilitation of metadata enhancement for data discovery. One of the goals of the project is to develop and apply new geospatial technologies, tools, and methods, including artificial intelligence (AI) and machine learning (ML), to help solve complex agricultural problems that require collaboration across scientific disciplines and geographic locations. In addition, the project will rely on the synthesis, integration, linkage, and analysis of large, diverse datasets that benefit from the high-performance computing (HPC) capabilities provided by the USDA ARS SCINet Program. 

Learning Objectives: The objective of this opportunity is to facilitate high-impact agricultural science and the participants’ professional growth via collaborative research, professional mentoring, and training in geospatial analysis, visualization and mapping, and AI and ML techniques.

Mentor(s)The mentor for this opportunity is Luis Rodriguez. If you have questions about the nature of the research please contact John Humphreys (

Anticipated Appointment Start Date: November 2022.  Start date is flexible and will depend on a variety of factors.

Appointment LengthThe appointment will initially be for one year, but may be renewed upon recommendation of ARS and is contingent on the availability of funds. 

Level of ParticipationThe appointment is full-time.

Participant StipendThe participant will receive a monthly stipend commensurate with educational level and experience.

Citizenship RequirementsThis opportunity is available to U.S. citizens only.

ORISE InformationThis program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and ARS. Participants do not become employees of USDA, ARS, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE.

Questions: Please visit our Program Website. After reading, if you have additional questions about the application process please email and include the reference code for this opportunity.

The qualified candidate should have received a master's or doctoral degree in one of the relevant fields (e.g. Geography and Geosciences, Computer and Data Sciences, Earth and Environmental Sciences, Quantitative Biological and Ecological Sciences, Health and Veterinary Sciences, Mathematics and Statistics), or be currently pursuing one of the degrees with completion before January 1, 2023.

Preferred skills:

  • Proficiency in geospatial analysis using at least one programming language (R and/or Python) or expertise in other GIS platforms.
  • Basic knowledge of applied statistics and spatial analysis
  • Experience working with databases and large datasets
  • Ability to effectively collaborate and work with others
  • Strong oral and written communication skills