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Rachel Cook

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PhD Research Assistantship in Spatial Modeling of Forest Growth and Resource Economics North Carolina State University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded 4-year PhD research assistantship starting January 1, 2027. Stipend is $30,000 per year for up to 4 years of direct support (2027–2030). Tuition, student fees, health insurance, computing equipment, travel funding, and page charges are fully covered via PI matching funds and university programs.

Deadline

Dec 15, 2026

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Country

United States

University

North Carolina State University

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Keywords

Computer Science
Environmental Science
Agriculture
Biology
Remote Sensing
Geography
Arboriculture
Economics
Resource Economics
Statistics
Econometrics

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About this position

North Carolina State University is advertising a fully funded PhD Research Assistantship in Spatial Modeling of Forest Growth & Resource Economics within the Department of Forestry and Environmental Resources.

The project sits at the intersection of quantitative forestry, forest biometrics, remote sensing, GIS, resource economics, and spatial modeling. The selected student will study how site-specific silvicultural interventions—especially fertilization and understory vegetation control—affect loblolly pine growth, landscape yield response, and economic returns under uncertainty across the southeastern United States.

Research activities include growth response modeling across 100+ field trial sites, building reproducible spatial workflows with satellite imagery and LiDAR, and developing spatially explicit financial and decision models under volatile input costs and stumpage prices. The student will collaborate with industry partners through the Forest Productivity Cooperative (FPC) and the Southern Forest Resource Assessment Consortium (SOFAC).

Funding includes a $30,000/year stipend for up to 4 years, with tuition, student fees, health insurance, computing equipment, travel funding, and page charges covered.

Applicants should have an M.S. in Quantitative Forestry, Forest Biometrics, GIS/Remote Sensing, Forest Economics, Applied Statistics, Data Science, or a related natural resource field. Strong B.S. applicants with substantial quantitative research experience may also be considered. Required skills include R or Python; experience with LiDAR, satellite imagery, raster analytics, and geospatial processing is strongly preferred. Experience with econometrics, Monte Carlo simulation, dynamic programming, or growth-and-yield modeling is a plus.

To apply, submit a single PDF containing a cover letter, CV, unofficial transcripts, and three professional references. Email the materials to [email protected] with the requested subject line. Deadline: 2026-12-15.

Funding details

Fully funded 4-year PhD research assistantship starting January 1, 2027. Stipend is $30,000 per year for up to 4 years of direct support (2027–2030). Tuition, student fees, health insurance, computing equipment, travel funding, and page charges are fully covered via PI matching funds and university programs.

What's required

Applicants should have an M.S. degree in Quantitative Forestry, Forest Biometrics, GIS/Remote Sensing, Forest Economics, Applied Statistics, Data Science, or a related natural resource discipline; highly qualified applicants with a B.S. and strong quantitative research experience may also be considered. Strong programming skills in R or Python are required, along with experience using remote sensing datasets such as LiDAR and satellite imagery. Knowledge of econometric analysis, Monte Carlo simulation, dynamic programming, or spatial growth-and-yield modeling is preferred. Applicants should also have a track record of scientific writing and clear communication.

How to apply

Prepare a single PDF with a cover letter, CV, unofficial transcripts, and contact information for three professional references. Email the application to Rachel Cook at [email protected] and use the specified subject line format. Apply by December 15, 2026.

More information can be found here

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