Imagine contributing directly to tools that help American farmers build healthier soils, conserve water, and maintain long-term productivity while protecting water quality across entire agricultural landscapes. That is the core of the USDA ARS Agricultural Watershed Modeling Fellowship, a specialized research opportunity administered through the Oak Ridge Institute for Science and Education (ORISE).
This part-time faculty appointment, offered by the U.S. Department of Agriculture’s Agricultural Research Service (ARS), invites current university faculty members who hold a doctoral degree to collaborate on cutting-edge improvements to process-based watershed models. The work sits at the intersection of hydrology, remote sensing, artificial intelligence, and regenerative agriculture—exactly the kind of multidisciplinary research that is reshaping how we manage natural resources under changing climate and land-use pressures.
Scholarship Summary
- Host Country: USA
- Host University: N/A (USDA ARS Hydrology and Remote Sensing Laboratory in Beltsville, Maryland or Ames, Iowa; research may also occur at participant’s home academic institution)
- Scholarship Type: PhD Scholarships
- Eligible Countries: USA
- Scholarship Benefits: Monthly stipend of $5,000–$18,000 (commensurate/negotiable), flexible part-time research appointment (minimum 20 hours per week), training in advanced watershed modeling and AI integration, collaboration with ARS scientists, opportunities for peer-reviewed publications, conference presentations, and development of decision-support tools
- Application Deadline: September 25, 2026 (applications reviewed on a rolling basis)
Why This Opportunity Matters Right Now
Agricultural watersheds face mounting challenges: nutrient runoff, variable precipitation patterns, groundwater depletion, and the need to scale regenerative practices such as cover crops and conservation tillage without sacrificing yields. Traditional process-based models capture physical processes well but often struggle with data integration, computational efficiency, and predictive skill at management-relevant scales.
The USDA ARS Agricultural Watershed Modeling Fellowship targets these gaps head-on. Fellows will help refine numerical representations of key processes—especially evapotranspiration and groundwater movement—while incorporating high-resolution remote sensing data on agricultural management and water-quality variables. A distinctive feature is the explicit integration of artificial intelligence methods with established process-based models to improve interpretability, accuracy, and predictive power.
Outcomes are expected to strengthen open-source watershed models already widely used by researchers and agencies, generate new data products and methods, and produce practical decision-support tools. Results will be shared through peer-reviewed publications, conference presentations, and stakeholder-focused resources that support soil health, resource conservation, and agricultural profitability.
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Research Focus and Daily Work
The primary laboratory affiliations are the Hydrology and Remote Sensing Laboratory in Beltsville, Maryland, or facilities in Ames, Iowa. Faculty participants may also conduct research at their own academic institutions during the academic year, provided a clear communication and mentoring plan is established with the ARS mentor.
Key activities include:
- Improving process representations of hydrological and biogeochemical cycles in agricultural landscapes.
- Integrating remote sensing–derived layers (cover crops, conservation tillage, soil moisture, nutrient concentrations) to drive and evaluate model performance.
- Combining process-based models with AI techniques to enhance predictive capability and scientific insight.
- Collaborating across institutions on multidisciplinary projects that link field observations, geospatial analysis, and model development.
The mentor for this opportunity is Dr. Xuesong Zhang (xuesong.zhang@usda.gov), a Research Physical Scientist at the Beltsville lab whose work focuses on regional- and global-scale hydrologic and agroecosystem modeling, assimilation of remote sensing data, and coupled water–carbon–nutrient cycling. Applicants are encouraged to contact him with specific questions about the research direction.
What Fellows Will Gain
The appointment is designed as a genuine learning experience rather than a standard research contract. Participants can expect to:
- Master advanced watershed modeling approaches used to study hydrological and biogeochemical processes in agricultural systems.
- Gain hands-on experience improving numerical schemes for evapotranspiration, groundwater flow, and related processes.
- Develop expertise integrating remote sensing and time-series datasets into environmental models.
- Build skills in machine learning and statistical methods (including Bayesian approaches where relevant) applied to large, diverse agricultural datasets.
- Strengthen scientific computing abilities across languages commonly used in the field—Fortran, C/C++, R, Python, and Matlab.
- Practice communicating results to both scientific audiences and stakeholders who need actionable decision-support tools.
- Contribute to the broader goal of designing regenerative practices that sustain soil health and agricultural profitability.
Because the appointment is part-time (at least 20 hours per week) and can be structured as a sabbatical or academic-year collaboration, faculty members can continue their primary university responsibilities while expanding their research portfolio and professional network within the ARS research enterprise.
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Eligibility, Appointment Terms, and Compensation
This opportunity is open exclusively to U.S. citizens who hold a doctoral degree and currently serve as faculty members at an accredited U.S. institution of higher education. Preferred backgrounds include experience with process-based agricultural watershed modeling, geospatial and time-series analysis, machine learning, large datasets, and strong computational and communication skills.
The initial appointment is for one year and may be renewed based on ARS recommendation and funding availability. The anticipated start is after October 1, 2026, with flexibility depending on individual circumstances.
Stipend ranges from $5,000 to $18,000 per month and is commensurate with experience and negotiated on an individual basis. Participants are not employees of USDA, ARS, the Department of Energy, or ORAU; the program is educational in nature. Proof of health insurance is required and can be obtained through ORISE if needed.
Applications are reviewed on a rolling basis, with a final deadline of September 25, 2026.
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How to Apply and Next Steps
The official application portal is Zintellect. Interested candidates should prepare:
- A completed ORISE application
- A statement of research interests
- A current curriculum vitae that includes academic and employment history, relevant experience, and a publication list
- Two educational or professional recommendations
All materials must be submitted in English (or accompanied by official translations). Full details and the application form are available at the opportunity page: https://www.zintellect.com/Opportunity/Details/USDA-ARS-NEA-2026-0297.
Additional program information can be found on the ORISE USDA-ARS Research Participation Program site and the original posting on the Natural Resources Job Board.
For questions about the application process, contact ORISE.ARS.Northeast@orau.org and reference USDA-ARS-NEA-2026-0297.
The Bigger Picture: Building Tools for Regenerative Agriculture
Watershed models are no longer just academic exercises. They underpin conservation planning, water-quality assessments, and evaluations of practices that keep farms productive while protecting downstream ecosystems. By improving the physical fidelity of these models and pairing them with modern data streams and AI methods, this fellowship contributes to a practical toolkit that farmers, conservation districts, and policymakers can use.
Faculty members who participate gain new technical skills, expand their collaborative networks, and produce publications and tools that strengthen both their own research programs and the broader ARS mission. In an era when agricultural systems must simultaneously feed a growing population and adapt to environmental pressures, work of this kind has clear societal value.
If your research interests align with process-based modeling, remote sensing of agricultural landscapes, AI applications in hydrology, or regenerative management practices, this USDA ARS Agricultural Watershed Modeling Fellowship offers a structured, well-supported pathway to advance that work. Review the full opportunity details, contact the mentor with research questions, and submit your materials well before the September 25, 2026 deadline. The models—and the landscapes they help protect—will be better for it.
Relevant resources:
- Application portal: https://www.zintellect.com/Opportunity/Details/USDA-ARS-NEA-2026-0297
- ORISE USDA-ARS program overview: https://orise.orau.gov/usda-ars/
- USDA Agricultural Research Service: https://www.ars.usda.gov/
- Natural Resources Job Board posting: https://jobs.rwfm.tamu.edu/view-job/?id=118012
This opportunity represents one of the more flexible and scientifically ambitious faculty research appointments currently available through the ARS–ORISE partnership. For eligible researchers ready to deepen their expertise in agricultural watershed science, the door is open.