Oklahoma State University is offering a NASA-funded PhD assistantship in the HI-GRASS (Holistic Investigation of Grassland Systems across Scales) project, supervised by Associate Professor Hamed Gholizadeh. The research focuses on integrating imaging spectroscopy, or hyperspectral remote sensing, from unoccupied aircraft systems (UASs/drones), airborne, and spaceborne platforms with in situ observations such as plant diversity, soil microbial and fungal diversity, and animal telemetry. The goal is to uncover the factors influencing aboveground and belowground biodiversity in grassland ecosystems.

The ideal candidate will have a Master’s degree in remote sensing or a related field by the time of appointment, with strong programming skills (Python, R, MATLAB) for remote sensing data analysis. Experience in hyperspectral remote sensing, statistical methods, and first-author peer-reviewed publications are highly valued. The position requires the ability to work both independently and collaboratively, excellent communication skills, enthusiasm for grasslands, and comfort with fieldwork in remote settings. Additional qualifications include familiarity with high performance computing, an FAA remote pilot license, and knowledge of grassland ecosystems.

This assistantship is fully funded by NASA, though specific stipend and tuition details are not provided. The target start date is May 2026, and for full consideration, applications should be submitted by January 15th, 2026. Interested applicants should contact Dr. Hamed Gholizadeh via email with a CV and a brief cover letter outlining research and career interests. A full application to the Graduate College will be required for an official offer. The position will remain open until filled.

Key research areas include hyperspectral remote sensing, biodiversity, grassland ecosystems, and advanced data analysis. This opportunity is ideal for students passionate about environmental science, biology, and earth science, and who are eager to contribute to cutting-edge research in grassland biodiversity using state-of-the-art remote sensing technologies.