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Virginia Tech

Fully Funded PhD in Agroecosystem Modeling, AI, and Sustainable Agriculture at Virginia Tech Virginia Tech in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Sep 1, 2026

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Country

United States

University

Virginia Tech

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Keywords

Computer Science
Data Science
Environmental Science
Agriculture
Biology
Remote Sensing
Soil Science
Artificial Intelligence
Earth Science
Sustainable Agriculture
Agroecology
Statistics
Machine learning

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About this position

Virginia Tech’s Agroecosystem Modeling Lab is recruiting a highly motivated PhD student for a fully funded USDA-NIFA research project starting in Spring 2027.

The project focuses on agricultural diversification practices, especially cover cropping and crop rotation, and their effects on crop yield, soil health, and soil biogeochemical cycling.

Research methods include meta-analysis, process-based agroecosystem modeling (such as DSSAT, APSIM, and DNDC), artificial intelligence, and large geospatial data analysis. This is a strong fit for students interested in agriculture, agroecology, agronomy, soil science, environmental science, remote sensing, data science, and computational modeling.

Ideal applicants should hold an MSc in agroecology, agronomy, soil science, hydrology, remote sensing, or a related field. Experience with crop or ecosystem models is preferred, and programming skills in Python, R, MATLAB, or C++ are expected. Backgrounds in AI, high-performance computing, or data assimilation are a plus.

The position is fully funded and includes tuition, stipend, and benefits. The application deadline is September 1, 2026.

Interested candidates should contact Dr. Yongfa You by email before applying and include a brief research statement, CV, academic transcripts, and three references.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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