We are recruiting Ph.D. students and Postdoctoral Researchers in High-Performance Computing!
I’m Dr. Jiaze He, a tenured professor in the Department of Astronautical Science and Mechanics and a Deputy Director at the International Center for Applied Mechanics at the Harbin Institute of Technology (HIT) in China. I warmly invite you to join my group (Lab for Computational Imaging and Smart Measurements) as a Ph.D. student (English-taught program in Mechanics) or a Postdoctoral Researcher in the School of Astronautics.
Before joining HIT, I served as a tenure-track assistant professor in the Department of Aerospace Engineering and Mechanics at the University of Alabama, a postdoctoral research associate in the Theoretical & Computational Seismology group at Princeton University, a research scholar at NASA LaRC, and an adjunct assistant professor in MAE at NCSU. I have led numerous national-level research projects in both the U.S. and China. I serve on the ASME NDPD Executive Committee, the SAE AMS K Committee, and is the Chair of the Congress-Wide Symposium on NDE & SHM at ASME IMECE. I am the recipient of the 2024 ASME SMASIS SHM Technical Committee Best Paper Award, the 2025 Smart Materials and Structures Emerging Leader Award, and the Best Paper Award at the 11th IWSHM.
My lab develops and implements cutting-edge computational ultrasonic imaging methods. Because of recent advancements in high-performance computing and quantitative imaging methods, our group will take the opportunity to push the limits on structural, material, and medical applications. We have fully-funded Ph.D. openings and Postdoctoral openings in the area of High-Performance Computing for Imaging and AI:
Background:
Computational science, applied mathematics, mechanical or aerospace engineering, computer science, electrical engineering, geophysics, or biomedical engineering
Required Skills:
- Parallel programming (MPI, OpenMP) and GPU acceleration (CUDA, OpenCL, or PyTorch)
- Experience with HPC clusters and job schedulers (e.g., SLURM)
- Numerical methods for PDEs or wave propagation (e.g., FDTD, FEM, spectral methods)
- Large-scale data handling and memory-efficient computing
- Machine learning frameworks (e.g., PyTorch) for physics-informed modeling or hybrid inversion
- Experience with imaging algorithms or scientific visualization (Paraview, matplotlib, etc.) is a plus
Related information:
Interested candidates are encouraged to contact Prof. Jiaze He (jiazehe@hit.edu.cn ) with a CV.