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OSU-Stillwater

Dr. Yuting Zhou, yuting.zhou@okstate.edu

TBD

12 months or less

Commensurate with education and experience

While applications will be accepted until a successful candidate has been hired, interested parties are encouraged to submit their materials by May 20, 2026, to ensure full consideration.

Interested applicants should submit:
1) Cover Letter: a one-page summary of research expertise specifically related to the project
2) a detailed Curriculum Vitae (CV) including educational background, research experience, publications, and contact information for three or more professional references
Questions about this position can be directed to Dr. Yuting Zhou (yuting.zhou@okstate.edu) and Dr. Pradeep Wagle (pradeep.wagle@usda.gov).

For a collaborative project between Oklahoma and Central Plains Agricultural Research Center (OCPARC) at the USDA-ARS, El Reno, OK (https://www.ars.usda.gov/plains-area/el-reno-ok/ocparc/) and the Department of Geography at Oklahoma State University (https://cas.okstate.edu/geography/), we are seeking a motivated Post-doctoral Researcher. This role focuses on quantifying the complex interactions between management practices, cropping systems, and weather variability on carbon and water cycles across diverse agroecosystems, including native prairie and tame pastures, winter wheat, alfalfa, and different summer cash or cover crops.

Responsibilities:

The successful candidate will hold a 100% research appointment and will be located at the USDA-ARS, El Reno, OK. The candidate will also work with collaborative teams at the University of Oklahoma. By synthesizing eddy flux data from an unparalleled network of 17 eddy covariance (EC) towers at the USDA-ARS, El Reno, OK with multi-source satellite remote sensing (e.g., Landsat, Sentinel, and CubeSats), high-resolution UAV imagery, and site-specific meteorological records, the researcher will evaluate resource use efficiency in various agroecosystems, specifically light, carbon, and water use efficiency, under varying land-use intensities (e.g., burning, grazing, and till vs. no-till, irrigated vs. rainfed). The researcher will contribute to the development of predictive agroecosystem models designed to optimize agricultural productivity while enhancing environmental resilience. The successful candidate will be jointly supervised by Dr. Yuting Zhou (Oklahoma State University) and Dr. Pradeep Wagle (USDA-ARS).

Salary and benefits:

Salary will be commensurate with education, experience, and qualifications, and is contingent on available funding. Benefits include comprehensive medical plans. Information on benefits can be found at https://hr.okstate.edu/benefits/index.html.

Employment conditions:

This is a full-time (100%), 12-month temporary research, non-tenure track position. An extension for a second year is possible, contingent upon the successful performance of the candidate and available funding. Ideal start date is June 1, 2026.

About OSU:

Oklahoma State University is a Carnegie Tier-1 university with excellent research facilities. Oklahoma State University is located in Stillwater, OK, rated as the friendliest college town in the U.S. Because of its mid-continent location that spans a broad expanse of habitats, Oklahoma has both prairie and forest ecosystems that support an exceptional level of biodiversity.

• Ph.D. degree (at the time of appointment) in biometeorology, geography, ecology, agronomy, remote sensing, or a closely related field.
  (degree must be conferred on or before agreed upon start date)

• Familiarity with field measurements, field sampling, and high-performance computing environments.

• Skills, Proficiencies, and/or Knowledge:
  • Strong programming and statistical skills (e.g., R, Python, MATLAB) for handling large, complex eddy fluxes, meteorological records, and remote sensing data.
  • Proven ability to work both independently and in a collaborative, multi-disciplinary setting.
  • Excellent written and oral communication skills, evidenced by first-author peer-reviewed publications in reputed journals.