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.

We are looking for a Research Associate to develop novel AI methods for process monitoring in laser-based metal additive manufacturing. The role will focus on thermal field modelling, multi-physics numerical simulations, machine learning and process parameter optimization. We expect the candidate to use data-efficient AI methods to accurately predict process anomalies and material defects during the printing process, to further achieve superior part quality and process stability and sustainability.

Key Responsibilities:

The Research Associate will work on a cross-disciplinary project at the intersection of fluid mechanics, numerical simulation, design optimization and machine learning. The key responsibilities of this position include:

Collect thermal data from metal additive manufacturing process
Develop cost-efficient and data-efficient AI models
Perform process anomaly and material defects detection
Implement real-time process monitoring for layer-wise printing
Collaborate with internal and external stakeholder on energy systems
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 thesis-based undergraduate and master students
Assist in proposal writings

Job Requirements:

Required educational qualification, experience, skills and competencies for this position:

Master's degree or above in Mechanical Engineering, Structural Engineering, Energy Engineering, Industrial Engineering, Machine Learning, or related fields.
Expertise in laser-based metal additive manufacturing process
Expertise in data-driven and AI/machine learning research
Proficiency in basics of programming languages such as C++, Python, and/or Matlab
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
Strong analytical and problem-solving skills to analyze and interpret research data
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
Excellent verbal and written communication skills
Publication track record is an advantage for this position

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

Research Associate (AI-based Process Monitoring)