Graduate Student Opportunity (Ph.D. or M.S.) – Fall 2027 | COMPOUND Flood Lab, UT Austin, USA

The University of Texas at Austin; Austin (TX); On-site; USD 24,000 - 36,000; Part time

Job summary

The University of Texas at Austin's COMPOUND Flood Lab seeks a motivated graduate student (Ph.D. or M.S.) to start in Fall 2027. Research areas include compound flooding, debris–structure–wave interactions, and flood risk assessment using numerical modeling, machine learning, and remote sensing.

Applicants from Civil/Coastal/Ocean/Hydrology/Hydraulic Eng. are encouraged; Ph.D. applicants with a master’s degree are preferred.

Qualifications

• Ph.D. or M.S. in Civil/Environmental Engineering; master’s preferred for Ph.D. applicants.
• Experience with numerical modeling, ML, remote sensing, or HPC is a plus.
• Interest in LLM-based research workflows and related software is desirable.

Responsibilities

• Join COMPOUND Flood Lab and contribute to fall 2027 onboarding.
• Conduct research on compound flooding and related interactions using modeling and ML.
• Collaborate with faculty and students; pursue RA/TA opportunities and disseminate results.

Skills

Numerical modeling

Machine learning

Remote sensing

High-performance computing

Education

Ph.D. in Civil Engineering

M.S. in Civil Engineering

Tools

ADCIRC

Delft3D

OpenFOAM

MPM

SFINCS

Job description

I am seeking one motivated graduate student (Ph.D. or M.S.) to join the COMPOUND Flood Lab in the Maseeh Department of Civil, Architectural and Environmental Engineering at The University of Texas at Austin in Fall 2027. I am particularly interested in students whose research interests include compound flooding, debris–structure–wave interactions, and/or flood risk assessment, using numerical modeling, machine learning, and remote sensing. The position is expected to be supported through a research assistantship and/or teaching assistantship.

Applicants with backgrounds in Civil Engineering, Coastal Engineering, Ocean Engineering, Hydrology, Hydraulic Engineering, or related fields are encouraged to apply. For Ph.D. applicants, a master’s degree is preferred. Experience with numerical modeling (ADCIRC, Delft3D, SFINCS, OpenFOAM, MPM, etc.), machine learning (PIML, GNNs, transformers, neural operators, etc.), remote sensing, or high-performance computing is a plus. I am also particularly interested in applicants who have developed software or research workflows using large language models (LLMs).

For more information about my research and current projects, please visit the COMPOUND Flood Lab website: https://sites.utexas.edu/junwhanlee/

Due to the volume of inquiries, I may not be able to respond to every email individually. Thank you for your understanding.

Please feel free to share this opportunity with students who may be interested.