Senior Research Engineer II (Robot Learning & Manipulation)

About the role

NTU is seeking a Senior Research Engineer with a strong robotics learning background to lead research on visuomotor manipulation policies for humanoid robots, balancing advances in the state of the art with real-robot deployment. The role sits within the NTU Main Campus.

Key responsibilities

• Lead research and development of learning-based visuomotor policies for humanoid robot manipulation, from problem formulation to real-world validation.
• Define research directions and technical roadmaps for grasping, object reorientation, bimanual manipulation and assembly.
• Advance techniques such as behaviour cloning, reinforcement learning and VLA-based reasoning, aiming for novel contributions and publishable outcomes.
• Address robustness challenges such as sensor noise, partial observability, contact dynamics and environment variability.
• Oversee the research pipeline from data collection and model training to evaluation and deployment.
• Manage milestones, deliverables and timelines, coordinate with collaborators and stakeholders, and report progress to the PIS.
• Supervise and mentor research associates, engineers and PhD students.
• Work with perception, controls, systems and hardware teams to integrate learned policies into the full autonomy stack, and evaluate learning-based versus classical approaches.
• Lead the preparation of publications, technical reports and grant/project documentation.

Required profile

• PhD in Robotics, Computer Science, Electrical Engineering or a related field.
• Strong publication record in robot learning, manipulation or embodied AI at venues such as CoRL, RSS, ICRA, IROS, NeurIPS or CVPR.
• Demonstrated experience developing and deploying robot learning systems on physical platforms.
• Proven ability to define research problems, design experiments, analyse results and lead projects to completion.
• Experience leading, supervising or mentoring researchers, engineers or students.

Required skills

• Expertise in robot manipulation and visuomotor control.
• Strong knowledge of behaviour cloning, reinforcement learning, imitation learning or similar methods.
• Proficiency in Python and modern deep learning frameworks such as PyTorch.

Preferred: experience with humanoid or highly dexterous robots, translating research into deployed systems, and a track record of collaboration with industry or research partners. Only shortlisted candidates will be notified.