USDA-ARS Postdoctoral Fellow - AI for Integrated Weed Science and Breeding

Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-MWA-2026-0389 How To Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. Application Deadline 12/4/2026 3:00:00 PM Eastern Time Zone Description Applications are reviewed on a rolling-basis. ARS Office/Lab and Location A research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located in Urbana, Illinois. Research Project This ORISE fellowship offers an opportunity to advance Integrated Weed Science and crop-breeding innovation through artificial intelligence and data-driven approaches. You will help develop machine-learning tools that enhance decision-making for weed management across the U.S. Cornbelt and key soybean-growing regions. The project brings together multi-institutional herbicide-performance datasets, environmental and management variables, and crop-yield outcomes to build predictive, interpretable models that support agricultural resilience. Learning Objectives Under the guidance of a mentor, you will be able to learn to: apply artificial intelligence, statistical modeling, and agricultural data science, develop methods in predictive analytics, computational agronomy, and evaluate decision-support tool development for modern weed science and crop-breeding applications. Mentor The mentor for this opportunity is Zhanyou Xu (View email address on click.appcast.io). If you have questions about the nature of the research, please contact the mentor(s). Anticipated Appointment Start Date December 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 ARS and is contingent on the availability of funds. Level of Participation The appointment is full time. 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. After reading, if you have additional questions about the application process, please email View email address on click.appcast.io and include the reference code for this opportunity. Qualifications The qualified candidate should be currently pursuing or have received a master's or doctoral degree in the one of the relevant fields. Preferred Skills Background in machine learning, artificial intelligence, or statistical modeling applied to agricultural or biological systems. Experience with large, multi-source agricultural datasets (environmental, management, yield, and herbicide-performance data). Skills in predictive modeling, quantitative analysis, and data integration across heterogeneous datasets. Familiarity with computational agronomy, weed science, crop breeding, or related plant-science disciplines. Experience with programming languages commonly used for data science (e.g., Python or R). Experience or interest in image-based phenotyping using satellite imagery and UAV (drone) image data. Communication skills, such as presenting research outputs to scientific collaborators and stakeholder audiences. Stipend $70,000.00 – $90,000.00 Yearly Point of Contact Michele Eligibility Requirements Citizenship: U.S. Citizen Only Degree: Master\'s Degree or Doctoral Degree. Discipline(s): Chemistry and Materials Sciences (Chemistry and Materials Sciences) Analytical Chemistry Bio-inorganic Chemistry Bio-organic Chemistry Biophysical Chemistry Chemistry (General) Computer, Information, and Data Sciences Engineering Life Health and Medical Sciences Mathematics and Statistics #J-18808-Ljbffr