The candidate will study, via computational modeling and analytical techniques the flow of complex fluids through complex domains, subject to non-linear conditions such as, phase change, and temperature and pressure dependence of the fluid properties.
- An undergraduate degree in mechanical engineering, aerospace engineering or other related fields.
- Substantial experience in Bayesian filtering and smoothing along with their application in thermal-fluid systems.
- Experience in physical field reconstruction algorithms via reduced order modelling and compress sensing theory is preferred.
- Good knowledge in computational fluid dynamics, and coding skills in MATLAB, C++, or Python.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Mechanical Engineering
Employee Referral Eligible: No
Job requisition ID : 22530