Research Fellow, Mechanics and Physics
The candidate will perform data-driven modelling of complex fluid motion, including single and multi-phase flow, Newtonian and non-Newtonian flows, as well as isothermal and non-isothermal flows.
- Substantial experience in Computational Fluid Dynamics and high-performance computing.
- Strong programming and analytical skills are required.
- Capable of developing deep learning techniques to model and control complex fluid flows.
- Good publication record in peer-reviewed journals for fluid mechanics.
- A Ph.D. degree in relevant fields related to Mechanics, Applied Mathematics, Computer Science, and Mechanical Engineering.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Mechanical Engineering
Employee Referral Eligible: No
Job requisition ID : 20033