Description
A position for Research Assistant in the field of learning-based control in surgical robotics is currently available in the Department of Mechanical and Automation Engineering. We are seeking a highly motivated and skilled Research Assistant to join our research group focused on advancing the field of general surgical automation. This position offers a significant opportunity to contribute to cutting-edge research at the intersection of robotics, artificial intelligence, and medicine.
The appointee will play an assistant role in the development and implementation of autonomous learning-based robotic systems for complex surgical procedures, working with both advanced simulations and physical hardware, with a special focus on multi-arm precise automation and development of embodied intelligence for general surgical systems, e.g. dVRK system.
Applicants should have (i) a Bachelor’s or a Master’s degree in Computer Science, Robotics, Engineering or related disciplines; (ii) familiarity with the theoretical foundations of imitation learning and reinforcement learning; (iii) hands-on experience with at least one robotics simulator, including NVIDIA Isaac-Sim, Mujuco, Gazebo, Pybullet, CoppliaSIM, etc; (iv) solid understanding and practical experience with deep learning frameworks such as Pybullet, Pytorch; (v) strong problem-solving skills and project execution capability; and (vi) self-motivation to work independently and co-operatively with other team members. Other preferred qualifications: (i) prior experience with NVIDIA Isaac Sim; (ii) hands-on experience with ROS/ROS2 and multi-robot control or coordinated manipulation; (iii) demonstrated expertise in rigid/soft body simulation and manipulation; (iv) previous involvement with physical robotic hardware and real-world experiment debugging; and (v) a keen interest in the application of embodied AI to surgical robotics.
The appointee will be responsible for (a) providing technical support to build the realistic surgical simulation platform based on Isaac-SIM, including rigid and soft object manipulation; (b) implementing and evaluating the state-of-the-art open-source imitation learning (IL) and reinforcement learning (RL) models to surgical tasks; (c) contributing to bridging the gap between simulation and reality by transferring trained policies to physical robotic setups; (d) providing assistance in setting up, debugging, and running real-world experiments with robotic arms and surgical instruments; and (e) co-operating with others in warehouse operations, purchasing and work preparation.
Appointment will initially be made on contract basis for one year, renewable subject to good performance and mutual agreement.
Applicants must submit a detailed CV, and copies of certificates showing that they have fulfilled the academic qualifications stated above in the online application, otherwise their applications will NOT be considered.
The University only accepts and considers applications submitted online for the post above via the CUHK career site.
Research Assistant • Shatin, NT
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