Research Fellow (Robot Learning & Manipulation)

SHARE@NTU Corporate Laboratory focuses on the development of key technologies for humanoid robotics, aimed at enabling intelligent services, industrial assistance, and human-centric applications. The research areas include multimodal sensing, artificial intelligence, environmental perception and situational awareness, as well as real-time motion planning and decision-making, allowing humanoid robots to operate safely and efficiently in complex and dynamic environments. Through the development of advanced humanoid robotic platforms, SHARE@NTU Lab seeks to address the growing global demand for automation while cultivating the next generation of local talent in robotics, artificial intelligence, and intelligent sensing technologies.

Our Lab aims to hire a Research Fellow with strong robotics learning background to help develop and improve our visuomotor manipulation policies, with a heavy emphasis on real-robot deployment.

Key Responsibilities:

• Lead the 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 manipulation capabilities such as grasping, object reorientation, bimanual manipulation, and assembly
• Advance techniques including behavior cloning, reinforcement learning, and VLA-based reasoning, with a focus on novel contributions and publishable outcomes
• Develop principled approaches to robustness challenges such as sensor noise, partial observability, contact dynamics, and environment variability
• Oversee the research pipeline from data collection strategy and model training to evaluation methodology and deployment
• Manage project milestones, deliverables, and timelines; coordinate with collaborators and stakeholders, and report progress to the PI
• Supervise and mentor research associates, engineers, and PhD students working on the project
• Collaborate with perception, controls, systems, and hardware teams to guide integration of learned policies into the full autonomy stack
• Evaluate tradeoffs between learning-based and classical approaches and make principled architectural and design decisions
• Lead the preparation of publications, technical reports, and grant/project documentation

Job Requirements:

• PhD in Robotics, Computer Science, Electrical Engineering, or a related field
• Strong publication record in robot learning, manipulation, embodied AI, or related areas (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, CVPR)
• Demonstrated experience developing and deploying robot learning systems on real robots
• Deep expertise in robot manipulation and visuomotor control
• Strong command of behavior cloning, reinforcement learning, or related learning-based manipulation methods
• Proficiency in Python and modern deep learning frameworks (e.g., PyTorch)
• Proven ability to independently define research problems, design experiments, and drive projects to completion
• Experience supervising or mentoring junior researchers or students

Bonus Qualifications

• Prior work on humanoids or highly dexterous robotic platforms
• Experience transitioning research prototypes into commercial or production robotic systems
• Track record of successful collaboration with industry partners
• Passion for building autonomous humanoid robots that operate in the real world

We regret that only shortlisted candidates will be notified.