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.

The School of Mechanical and Aerospace Engineering (MAE) is seeking to hire a Research Fellow to support the research in Physics-Informed Machine Learning (PIML) for metal additive manufacturing process. This role will focus on developing novel machine learning frameworks that seamlessly integrate physical principles with data-driven modeling for thermal and mechanical simulations in metal additive manufacturing. Candidates will design, implement, and validate PIML models that can accurately capture process physics such as heat transfer, melt-pool dynamics, residual stress evolution, and microstructure formation. Experiments and numerical simulations will be conducted to evaluate the predictive capability, robustness, and generalizability of the proposed models. The research aims to advance intelligent and reliable prediction, diagnosis, monitoring, and performance assessment of metal additive manufacturing processes.

The Research Fellow will work on a cross-disciplinary project at the intersection of mechanics, machine learning, and advanced manufacturing. The key responsibilities of this position include:

• Perform high-fidelity thermal and mechanical numerical simulations for metal additive manufacturing.

• Develop and implement PIML models for analysis and optimization of metal additive manufacturing.

• Integrate physical laws, experimental data, and simulation results into unified machine learning frameworks to improve model robustness and generalizability.

• Conduct data preprocessing, model training, and validation for machine learning tasks.

• Collaborate with internal and external stakeholders on metal additive manufacturing characterization, model validation, and system development.

• Maintenance of lab or equipment or supplies that include procurement and liaison with suppliers.

• Assist or produce high quality reports and documents that consolidate research findings.

• Mentoring PhD and thesis-based master students.

• Assist in grant proposal applications.

Requirements:

• PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or related fields.

• Strong background in Physics-Informed Machine Learning (PIML), scientific machine learning, or data-driven modeling for engineering systems.

• Expertise in numerical simulation of multi-physics systems, particularly thermal-mechanical analysis in metal additive manufacturing.

• Familiarity with finite element/finite volume methods, high-performance computing, and simulation data processing.

• Strong understanding of research methodologies, data analysis, and statistical techniques

• Ability to work both independently and collaboratively within a team

• Effective written and verbal communication skills, with the ability to convey information, collaborate and build relationship

• Ability to produce technical content for publications and deliver informative presentations to diverse audience including students

• Attention to details and a commitment to upholding ethical standards in all research activities

• Passion in the field of research and a desire to contribute to meaningful projects

• A strong publication track record in relevant fields (e.g., scientific ML, additive manufacturing, computational mechanics) will be an advantage

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

Hiring Institution: NTU