Description
We are seeking a highly motivated and talented
Postdoctoral Research Fellow to help develop a state-of-the-art digital twin in biofluid dynamics. This position lies at the intersection of
Physical AI,
high-fidelity computational fluid dynamics (CFD), and
advanced biomedical robotics.
The successful candidate will contribute to the development of two key components of our virtual testbed:
- Computational Fluid Dynamics for Blood Flow Simulation
- Biomechanical Simulation of Device-Tissue Interaction
Together, these capabilities will support the high-throughput, low-latency simulation environment required for training foundation models and embodied AI systems for next-generation endovascular robotic platforms in neurosurgery.
Core Responsibilities
- Advance Fluid-Structure Interaction (FSI):
Develop and expand our GPU-native, sharp-interface immersed boundary CFD framework to enable near-real-time, multi-GPU simulations of device-flow-clot-wall interactions under physiologically realistic pulsatile flow conditions.
- Multi-Physics Coupling:
Bridge hemodynamics and tissue mechanics by implementing a lightweight coupling interface that supports bidirectional force exchange and geometric constraint handling between asynchronous solvers.
Qualifications
Required Qualificatons
- PhD in Mechanical Engineering, Biomedical Engineering, Engineering Mechanics, or a closely related field.
- Demonstrated expertise in Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) modeling.
- Strong background in High-Performance Computing (HPC), including GPU-native solver development (e.g., CUDA) and parallel computing.
The referenced salary range is based on Johns Hopkins University's good faith belief at the time of posting. The actual compensation offered to the selected candidate may vary and will be based on factors including, but not limited to, the experience and qualifications of the selected candidate - e.g., years in rank, training, field, discipline, other work experience, and other similar factors; geographic location; internal equity; external market conditions; and other factors as reasonably determined by the University.