Research Fellow (Applied Machine Learning for Engineering Design)

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, Applied Machine Learning for Engineering Design to support efforts of the research group of Assistant Professor Song Binyang. The role will focus on data-driven design automation and generative modeling for AI-based engineering design and design for additive manufacturing.

Key Responsibilities:

• Conduct high-impact research on data-driven design, generative modeling, and design for additive manufacturing

• Supporting research grant applications

• Mentor students

• Guiding research projects within the research group

Job Requirements:

• PhD degree in Mechanical Engineering, Industrial Engineering, Electric Engineering, Computer Science, or related fields from reputable universities;

• Strong expertise in areas such as Engineering Design (e.g., Data-Driven Design, Generative Design, Design Theory and Methods), Human-AI Collaboration, and Additive Manufacturing;

• Proficiency in Applied ML / AI;

• Strong publication record in top-tier engineering design, human-AI interaction, AL / AI conferences / journals;

• Excellent communication skills, both spoken and written

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