Job Title: Research Engineer (AI-Driven Autonomous & Smart Manufacturing), Division of Industrial Design
University-Level Unit: College of Design and Engineering
Faculty/Department-Level Unit: Division of Industrial Design
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 13/04/2026
Job Description
NUS Centre for Additive Manufacturing (AM.NUS) is inviting applications for the position of Research Engineer in AI-Driven Autonomous and Smart Manufacturing, with a focus on Additive Manufacturing (AM).
- Develop AI-driven frameworks that integrate LLMs, agentic AI, and multimodal foundation models for autonomous and smart manufacturing applications, with emphasis on additive manufacturing.
- Fuse multimodal sensor data, reinforcement learning, and vision–language models to enable real-time defect detection, process monitoring, and intelligent decision-making.
- Support internal capability development and execute collaborative projects with academic researchers and industry partners.
- Assist in preparing technical reports, documentation, and contribute to publications and grant proposals where applicable.
- Assist laboratory safety and procurement whenever necessary.
AM.NUS leverages the National University of Singapore world-class faculties, expertise and advanced capabilities in 3D printing technology, to bring innovative products to the field of healthcare that will improve patient outcomes and change lives for the better.
AM.NUS explores a whole new dimension in medicine, applying ground-breaking 3D printing technology for personalised patient treatments.
AM.NUS is the only multi-disciplinary Additive Manufacturing (AM) centre in an Institute of Higher Learning in Singapore which comprises five Faculties (Medicine, Science, Engineering, Dentistry and Design & Environment). The Centre is supported both internally by NUS and externally by National Additive Manufacturing Cluster (NAMIC) and Singapore Economic Development Board.
Qualifications
- Bachelor degree in Mechanical Engineering, Manufacturing Engineering, Computer Science, Artificial Intelligence, Industrial Design or a closely related field.
- Strong background in additive manufacturing and smart manufacturing systems.
- Proven expertise in machine learning and AI, including experience with LLMs, multimodal models, or reinforcement learning.
- Experience with engineering design and simulation tools (e.g., AutoCAD, SolidWorks, FEA) is an advantage.
- Experience with digital twins, knowledge graphs, or cyber–physical systems is an advantage.
- Proficiency in written and spoken English.
