The Computational Water Research and Uncertainty Quantification Lab at the University of Hawaii at Manoa (http://www2.hawaii.edu/~jonghyun/group.html) is seeking applications for a PhD student in coastal engineering with an emphasis on stochastic inverse modeling/data assimilation, deep learning, and fast linear algebra software development in high performance computing (HPC) environment. Applicants with quantitative background in applied linear algebra and statistics, and experience in C++/Fortran/Python/Julia programming will be given preference.
Successful candidate will get involved in collaborative research with US Army Engineering Research and Development Center’s Coastal Hydraulics Laboratory (ERDC-CHL) and Stanford-Army High Performance Computing Research Center (AHPCRC) aimed at identifying nearshore environment variables and quantifying corresponding estimation uncertainties. As a part of the project, hydrodynamic model-based advanced data assimilation and deep learning techniques will be applied to nearshore and riverine bathymetry identification. Research goals for this position include 1) application of sequential and variational assimilation methods for unmanned aircraft systems (UAS) imagery-based coastal and riverine bathymetry identification, 2) development and application of deep learning techniques/reduced order model for shoreline management designs, 3) release and management of data assimilation and deep learning software packages in a public domain repository. The PhD student will join the department of Civil and Environmental Engineering and Water Resources Research Center at the University of Hawaii at Manoa and is expected to work collaboratively with colleagues in ERDC-CHL and AHPCRC. HPC facility and travel are supported.
Interested applicants should email a CV, transcripts, GRE scores and a one-page statement of past and present research goals to Dr. Jonghyun Harry Lee ( ). Informal inquiries are also invited by email. Review of applications starts immediately and will continue until positions are filled.