University of South Dakota Graduate Research Assistantship in Sustainability

PhD Graduate Research Assistantship Summary

Host institution:
University of South Dakota, Department of Sustainability and Environment, Vermillion, South Dakota, United States.

Target audience:
Master’s Degree graduates in Earth Sciences, Computer Science, or related disciplines.

Funding source:
National Science Foundation Collaborative Grant.

Financial support:
Full tuition coverage for the entire PhD, twelve‑month stipend, full research support, travel funding.

Final date to receive applications:
August 5 2026.

The research project focuses on modeling drought, soil moisture variations, and dust flux across cropland and grassland ecosystems in the Northern Great Plains using advanced data analytics, machine learning, and deep learning. The doctoral fellow will process very high‑resolution satellite imagery and large spatial datasets, utilize high‑performance computing clusters, conduct field work, operate small unmanned aerial systems for model validation, and present findings at national and international conferences.

• Analyze environmental datasets and develop models for drought, soil moisture, and dust flux.
• Process very high‑resolution satellite imagery and other geographic data sources.
• Apply machine learning, deep learning, Bayesian statistics and other advanced techniques to large spatial datasets.
• Conduct and support outdoor field work in remote regions of the Northern Great Plains.
• Operate small unmanned aerial systems to collect observational data for model validation.
• Collaborate with regional teams and present scientific findings at conferences.

• Full tuition coverage: 100% tuition waiver for all PhD coursework and dissertation research.
• Twelve‑month stipend covering housing, meals, and local living expenses.
• Full research support: access to high‑performance computing resources, drone equipment, and software licenses.
• Travel funding for field work and conference presentations.

• Master’s degree in ecology, geography, environmental sciences, environmental engineering, computer science, computer engineering, or a related earth science discipline.
• Proficiency in Python, Geo‑AI tools, Google Earth Engine, and R programming.
• Strong background in machine learning, data visualization, model selection, Bayesian statistics, and handling large spatial datasets.
• Must reside in the United States; remote work not permitted.
• Willingness and physical ability to conduct outdoor field work in remote areas and operate small unmanned aerial systems.

August 5 2026.