National University of Singapore (NUS) Jobs

Research Engineer (AI-Driven Autonomous & Smart Manufacturing), Division of Industrial Design

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National University of Singapore (NUS)

Kent Ridge Campus

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Research Engineer (AI-Driven Autonomous & Smart Manufacturing), Division of Industrial Design

Research Staff

13 June 2026

Location

Kent Ridge Campus, Singapore

National University of Singapore

Type

Research Staff

Start Date

13 April 2026

Required Qualifications

Bachelor's in Mechanical Engineering/CS/AI/Industrial Design
Expertise in AI/ML (LLMs, multimodal models, RL)
Additive Manufacturing & Smart Manufacturing background
Proficiency in English

Research Areas

AI-Driven Autonomous Manufacturing
Additive Manufacturing
Smart Manufacturing Systems
Multimodal Sensor Fusion
Reinforcement Learning & Vision-Language Models
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Research Engineer (AI-Driven Autonomous & Smart Manufacturing), Division of Industrial Design

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.

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Frequently Asked Questions

🎓What qualifications are required for this Research Engineer role at NUS?

Candidates need a Bachelor's degree in Mechanical Engineering, Manufacturing Engineering, Computer Science, Artificial Intelligence, or Industrial Design. Essential: strong background in additive manufacturing and smart manufacturing, plus proven machine learning and AI expertise including LLMs, multimodal models, or reinforcement learning. Advantages include tools like AutoCAD, SolidWorks, FEA, digital twins, or cyber-physical systems. Proficiency in English is required. Explore similar research jobs or research role tips.

🔬What are the main responsibilities in AI-Driven Autonomous Manufacturing at AM.NUS?

Develop AI-driven frameworks integrating LLMs, agentic AI, and multimodal foundation models for autonomous manufacturing, focusing on additive manufacturing. Fuse multimodal sensor data, reinforcement learning, and vision-language models for real-time defect detection, process monitoring, and decision-making. Support projects, reports, publications, lab safety, and procurement. Check research assistant jobs for related opportunities.

📝How do I apply for this Research Engineer position at NUS?

Applications are open from 13 April 2026 until 13 June 2026. Click 'Apply now' in the job post on the NUS site. Prepare your CV highlighting AI, machine learning, and additive manufacturing experience. Tailor to smart manufacturing skills. Use our free resume template and cover letter template for academic applications.

🏭What is the NUS Centre for Additive Manufacturing (AM.NUS)?

AM.NUS is Singapore's leading multi-disciplinary additive manufacturing centre, spanning Faculties of Medicine, Science, Engineering, Dentistry, and Design & Environment. It advances 3D printing for healthcare, supported by NUS, NAMIC, and EDB. Focuses on AI-driven smart manufacturing and innovative products. Learn more via research jobs or research career advice.

📍What is the location and employment details for this smart manufacturing role?

Positioned at Kent Ridge Campus, National University of Singapore. Research Staff category, full-time. Posting starts 13 April 2026; apply by 13 June 2026. Involves collaborative projects with academics and industry. No salary or visa details specified. View higher ed jobs in Singapore for context.

⚙️Are there advantages for candidates with specific skills in this NUS role?

Yes, experience with engineering design tools (AutoCAD, SolidWorks, FEA), digital twins, knowledge graphs, or cyber-physical systems is advantageous for AI-driven additive manufacturing. Strong fit for those in autonomous systems and smart manufacturing. Boost your profile with academic CV tips.

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