Job Description

This project is to develop data-driven machine learning-based approximate models, which can replace the time consuming CFD simulations. The proposed work aims to provide proof of concept, methodology to develop machine learning models and its applicability in tasks such as flow flied prediction over variable geometries and geometrical optimization.

Qualifications

The proposed project extensively involves mathematical modelling, computer programming, and numerical simulation. It requires researchers to have strong background in mathematics and computational physics. The candidate must have a Masters degree and experiences in computational fluid dynamics and machine learning related area. The research experience in gas kinetic solvers is preferred.