Ph.D. opportunity at Texas A&M University
Texas A&M University, Texas, United States
Apply before: 30 December 2022

I am recruiting one graduate student at the PhD level to join the Fluvial Landscape Dynamics (FLUD) Group at Texas A&M University starting in Fall 2023. Funding will be available through a combination of graduate research assistantship for an NSF-funded project led by Dr. İnci Güneralp and teaching assistantship. Research topics will be centered around developing innovative nature-based solutions for effective and sustainable mitigation of river flooding and management of freshwater resources within lowland landscapes, where nature, people, and built environment interact and coevolve. There will be opportunities for collaborations with researchers from social and behavioral sciences and public policy.

The applicant is expected to have: (1) a background in a geomorphology, hydrology, or related fields (undergraduate or master’s degree); (2) experience in at least one of the following: hydromorphodynamic modeling, programing (Python, R), Geographical Information Systems, and remote sensing (multispectral, hyperspectral); (3) independent research and teamwork skills; and (4) strong written and oral communication skills.

Interested students are encouraged to contact me at for more information or to discuss their research interests. Please include a copy of your academic CV and brief statement of research interests. Texas A&M University is an Affirmative Action/Equal Opportunity Employer, and the University especially encourages applications from women and minorities. Applicants from underrepresented groups in geosciences are especially encouraged to apply.

Applications will be accepted through December 30, 2022. GRE scores are not required to apply. For more information on the application process, please visit

Thank you,

Inci Guneralp, Ph.D.
Associate Professor
Department of Geography
Texas A&M University

College Station, TX 77843-3147

Of interest for:
  • Terrestrial Working Group
  • Hydrology Focus Research Group
  • River Network Modeling Initiative