Research Fellow (Experimental Nanomechanics)

About the role

The School of Mechanical & Aerospace Engineering at Nanyang Technological University (NTU) is looking for a Research Fellow to lead research in in-situ experimental mechanics. The work focuses on developing MEMS-based characterization platforms inside SEM/TEM environments, and on investigating real-time mechanical failure, interfacial phenomena and mechanical-thermal-electrical coupled problems in low-dimensional materials, energy-storage systems and semiconductor devices. The role supports the university's mission to advance nanoscale metrology and AI-driven experimentation.

Key responsibilities

• Design, fabricate and assemble custom MEMS-based micro-stages and testing devices for in-situ mechanical and multiphysics characterization.
• Plan and run advanced in-situ SEM and TEM experiments to monitor mechanical failure, fatigue and dynamic behavior in real time.
• Lead high-impact journal publications and present findings at international conferences and university seminars.
• Assist in drafting research grant proposals, technical reports and progress updates for funding agencies and industry partners.
• Co-supervise PhD candidates and undergraduate researchers on experimental techniques, data interpretation and manuscript preparation.
• Oversee maintenance, calibration and safety protocols for key lab equipment.

Required profile

• Ph.D. in Mechanical Engineering, Materials Science and Engineering, Applied Physics, Micro/Nanotechnology or a related discipline.
• Proven track record in 2D material device fabrication, cleanroom microfabrication or MEMS processing.
• Extensive hands-on experience with SEM and TEM, ideally in in-situ mechanical, thermal or electrical testing platforms.
• A strong publication record in high-impact journals.
• Excellent written and oral communication, teamwork and collaboration skills in an interdisciplinary lab setting.

Required skills

• Data analysis and programming (e.g. Python, MATLAB) for image processing and instrument control.
• Experience with AI-driven or automated experimental workflows is an advantage.