The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

For more details, please view https://www.ntu.edu.sg/mae/research.

We are looking for a Research Fellow. The role will focus on helping to build thermal-flow engineering and bioengineering expertise in the School of Mechanical and Aerospace Engineering Research domain to establish itself as a World Class Centre and well recognized in Singapore and beyond, enhancing the academic excellence of NTU and attracting interest of more industrial collaborators.

Key Responsibilities:

• Develop a new and robust numerical model with lattice Boltzmann method to simulate flow and heat transfer with phase change processes.

• Carry out thorough a numerical investigation of the phase change characterizations.

• Examine underlying physical mechanisms and propose relevant physical scaling laws.

• Plan and carry out experiments.

• Be responsible for conducting high quality research, writing journal/conference papers, and assisting in student supervision and writing the project report.

Job Requirements:

• A PhD’s degree in mechanical engineering or related engineering field.

• Know multiple-scale and multi-phase numerical modeling and familiar with lattice Boltzmann simulations

• Having rich skills in coding in Fortran/C/C++ and high-performance computing

• Good knowledge in fluid mechanics, heat and mass transfer as well as surface phenomena.

• With good record in publishing research papers.

• Preferably with expertise in deep learning approaches and also experimental experience.

• Good communication and writing skills & team player.

• Strong interest in research work.

• Highly motivated, independent and resourceful.

We regret to inform that only shortlisted candidates will be notified.