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

Oak Ridge Institute for Science and Education; Urbana (IL); On-site; USD 70,000 - 90,000; Full time

Job summary

Oak Ridge Institute for Science and Education in Urbana, Illinois, invites applications for a full-time ORISE fellowship with the USDA ARS to advance integrated weed science using AI and data-driven approaches.

You will help develop machine-learning tools, work with multi-institutional datasets, and contribute to decision-support for weed management across the Cornbelt and soybean regions. Mentored by Zhanyou Xu, the position starts December 2026 with a one-year appointment and potential renewal.

Qualifications

• Master's or Doctoral degree in a relevant field.
• Background in machine learning, AI, or statistical modeling applied to agricultural or biological systems.
• Experience with large multi-source agricultural datasets (environmental, management, yield, herbicide-performance).
• Programming in Python or R.

Responsibilities

• Develop ML tools for weed management decision-support.
• Integrate datasets across environmental, management, yield, and herbicide-performance data.
• Collaborate with multi-institution partners to advance predictive models for agricultural resilience.

Skills

Machine learning

Artificial intelligence

Statistical modeling

Predictive analytics

Data integration

Python

R

Image-based phenotyping

Satellite imagery

UAV imagery

Communication skills

Education

Master's degree

Doctoral degree

Tools

Python

R

SQL

Job description

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 ([e-mail]). 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 [e-mail] 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