*Applications are reviewed on a rolling basis.

ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Sustainable Agricultural Systems Laboratory, located at Beltsville, Maryland.

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: Under the guidance of a mentor, the participant will have the opportunity to help facilitate access leading to the successful use of high-performance computing resources for scientific research. The participant will learn about the ARS Big Data Initiative (BDI; scinet.usda.gov) that is responsible for three major components: (1) a high-capacity network as the backbone of a research data and information conduit among ARS locations (SCINet); (2) high-performance computing and storage infrastructure available to all of the ARS scientific community; and (3) a virtual research support core (VRSC), a group of personnel with diverse skills in scientific computing that provides support and training to ARS scientists and staff. Furthermore, under the guidance of a mentor, the fellow will help to expand SCINet services through the construction of an open-access plant image repository and construct a GUI that enables ARS scientists to build synthetic image pipelines.

Learning Objectives:  The participant will learn about a wide range of activities related to organizational and operational planning, training coordination, and science communication activities in support of the BDI and the ARS AI Center of Excellence (AI-COE). The participant will also have the opportunity to take on-line courses in scientific topics, such as R, Python and statistics, and to learn collaboration and leadership skills through workshop and working group experience. Under the guidance of a mentor, the participant will be involved in helping to assess the state of plant image repositories in the US and develop a pathway to integrate new and past image repositories into a central resource for ARS scientists. The fellow will learn about constructing a GUI for synthetic image pipelines.

USDA-ARS Mentor: If you have questions about the nature of the research, Steven Mirsky, Sustainable Agricultural Systems Laboratory, steven.mirsky@usda.gov.

Anticipated Appointment Start Date: March 2026; Start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for one year but may be renewed upon recommendation of the mentor and ARS.

Level of Participation: The appointment is full-time.

Participant Stipend: The participant(s) will receive a monthly stipend commensurate with educational level and experience.

Citizenship Requirements: This opportunity is available to U.S. citizens only.

ORISE Information: This 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. If you have additional questions about the application process please email ORISE.ARS.SCINet@orau.org and include the reference code for this opportunity.

Qualifications

The qualified candidate should have received a bachelor's degree in one of the relevant fields. Degree must have been received within the past five years.

Preferred skills:

• Experience developing, testing, and refining machine learning models
• Experience developing HPC workflows
• Excellent written and oral communication skills.
• Experience in team and collaborative scientific environments.
• Experience constructing plant image repositories