Call for Ph.D. student applications: The Coastal Hazards Lab in the Department of Civil, Architectural, and Environmental Engineering (CAEE) at the University of Texas at Austin is recruiting an enthusiastic and self-motivated Ph.D. student to study coastal hazards using numerical modeling, machine learning, and drone observation. The expected start date is Fall 2023. The Coastal Hazards Lab encourages applications from members of groups underrepresented in STEM.

Information on the Coastal Hazards Lab can be found here: https://sites.utexas.edu/junwhanlee/.

Contact: Interested students are encouraged to email Dr. Jun-Whan Lee (junwhanlee@austin.utexas.edu) with the title [Prospective Ph.D. student]. Please provide (1) a one-page cover letter (describing prior research experience, interests, and career goal), (2) a CV with three reference contacts, and (3) unofficial transcript(s) in a single PDF file. If you do not wish to be considered for financial aid or have your funding or fellowship, please indicate it in the email. All emails will be reviewed on a rolling basis. I will interview applicants who are well-aligned with the lab and contact the applicants via email to inform them of their status.

How to apply: Please apply through the Graduate School website (https://gradschool.utexas.edu/how-to-apply). The application deadline is Dec 15, 2022.

Please get in touch with Dr. Jun-Whan Lee through email if you have any specific questions about the position.

Qualifications: A student with a bachelor’s or a master’s degree in civil engineering, coastal engineering, ocean engineering, geoscience, computer science, or a closely related field at the start of the appointment.

Desired skills: (1) a solid theoretical background in fluid mechanics and coastal process, (2) experience running numerical hydrodynamic models (ADCIRC, Delft3D, etc.) on high-performance computing systems (Linux, OpenMP, MPI, etc.), (3) strong programming skills (Python, MATLAB, etc.), (4) strong written and oral communication skills, (5) experience in machine learning (TensorFlow, PyTorch, etc.), (6) experience in remote sensing data (drone, Landsat, InSAR, Google Earth, etc.), and (7) an interest in interdisciplinary research.

Funding: The position will be funded on a combination of research and teaching assistantships, which include full tuition support, stipend, benefits, and travel support.