Research Fellow (AI for Thermal Management)

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.

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Key Responsibilities:

• Develop and implement high-fidelity CFD and FEA simulation workflows for modelling heat transfer, fluid-solid interactions, and pressure drop in complex thermal structures.

• Design and train physics-guided surrogate models (e.g. neural networks with embedded physical constraints) for rapid thermal prediction across geometries, materials, and scales.

• Contribute to the development of generative modeling frameworks for structural design of thermal management systems.

• Support experimental validation by correlating simulation and AI predictions with thermal measurements from fabricated prototypes.

• Collaborate closely with researchers at NTU and A.STAR, contribute to technical disclosures (IP), co-author high-quality journal publications, and assist in preparing proposals for future research projects.

• Mentor graduate and undergraduate students involved in the project.

Job Requirements:

• PhD in Mechanical Engineering, Aerospace Engineering, Materials Science, Computational Engineering, or a closely related field.

• Strong background in heat transfer, fluid mechanics, thermal management systems, and additive manufacturing.

• Hands-on experience with CFD/FEA tools (e.g. ANSYS, Abaqus, COMSOL, OpenFOAM).

• Solid programming skills in Python (experience with PyTorch/TensorFlow is highly desirable).

• Proven track record of peer-reviewed publications.

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