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 Associate with strong robotics learning background to help develop and improve our visuomotor manipulation policies, with a heavy emphasis on real-robot deployment.

Key Responsibilities:

• Design, train, evaluate, and deploy learning-based visuomotor policies for humanoid robot manipulation
• Develop manipulation behaviors such as grasping, pick-and-place, object reorientation, door opening, bimanual manipulation, and basic assembly
• Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning
• Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and environment variability
• Own the full pipeline from data collection on real robots to model training, evaluation, and deployment
• Work closely with simulation and digital twin tooling where useful, while prioritizing real-world performance and transfer
• Collaborate with perception, controls, systems, and hardware teams to integrate policies into a full autonomy stack
• Evaluate tradeoffs between learning-based and classical approaches and make principled design decisions
• Write high-quality, well-tested software that ships to and runs reliably on physical humanoid robots
• Partner with integration and testing teams to continuously improve robustness, performance, and deployment velocity

Job Requirements:

• Master in Robotics, Computer Science, Electrical Engineering, or a related field
• Hands-on experience developing and deploying robot learning systems on real robots
• Strong background in robot manipulation and visuomotor control
• Experience with behavior cloning, reinforcement learning, or related learning-based manipulation methods
• Proficiency in Python and/or C++ for robotics and ML systems
• Experience with modern deep learning frameworks (e.g., PyTorch)
• Ability to design experiments, analyze failures, and iterate quickly in real-world robotic systems
• Solid understanding of the tradeoffs between classical robotics approaches and learning-based methods
• Thrive in fast-paced, ambiguous environments where solutions require exploration and ownership

Bonus Qualifications

• Experience deploying learning-based manipulation systems in commercial or production robotic systems
• Prior work on humanoids or highly dexterous robotic platforms
• Publication record in robot learning, manipulation, or embodied AI
• Experience leading projects or mentoring other engineers
• Passion for building autonomous humanoid robots that operate in the real world

We regret that only shortlisted candidates will be notified.