Research Fellow – Physics‑Informed Neural Networks (PINNs)

About the role

The School of Mechanical & Aerospace Engineering at NTU seeks a Research Fellow to advance Physics‑Informed Neural Networks (PINNs) for quadruped robots. You will lead cutting‑edge research, integrate machine‑learning models with robotic control, and validate results experimentally.

Key responsibilities

• Design, execute, and analyse experiments, simulations, and theoretical studies related to PINNs on quadruped platforms.
• Lead and co‑author peer‑reviewed journal articles, conference papers, and technical reports.
• Plan project timelines, allocate resources, and report progress to the Principal Investigator.
• Develop an independent research profile and prepare for the next career stage.

Required profile

• PhD in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related discipline.
• Strong background in machine learning, PINNs, and control theory.
• Excellent verbal and written communication skills.
• Proficiency in Python and/or C/C++ programming.
• Curious, ambitious mindset with a strong work ethic and effective time‑management skills.

Required skills

• Python
• C/C++
• Machine learning
• Physics‑Informed Neural Networks (PINNs)
• Control theory