Research Associate (Robotics)

School of Electrical and Electronic Engineering is one of the founding Schools of the Nanyang Technological University. Built on a culture of excellence, the School is renowned for its high academic standards and research. With over 3,000 undergraduates students and 2,000 graduate students it is one of the largest EEE schools in the world and ranks 4th in the field of Electrical & Electronic Engineering in the 2025 QS World University Rankings by Subjects.

Today, the School has become one of the world’s largest engineering schools that nurtures competent engineers and researchers. Each year, the School graduates over a thousand students who are ready to take on great ambitions and challenges.

For more details, please view: https://www.ntu.edu.sg/eee

The project aims to develop robust, intelligent and efficient navigation systems for inspection robots, enabling autonomous localization, decision-making, motion planning and safe navigation in complex, dynamic real-world environments.

The Research Associate will support research on robot navigation, focusing on the integration of visual perception, localization, planning and navigation. The role will also investigate learning-based approaches, including diffusion models and reinforcement learning, for navigation decision-making, trajectory generation and adaptive robot behavior.

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  Design and develop navigation algorithms for inspection robots in complex and dynamic environments.

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  Work on global and local motion planning, obstacle avoidance, trajectory generation and trajectory tracking for humanoid robots.

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  Investigate diffusion models and diffusion-based policies for navigation planning, trajectory generation and action prediction.

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  Apply reinforcement learning and/or imitation learning methods to improve navigation performance and adaptive robot behavior.

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  Integrate visual navigation and planning modules with humanoid locomotion and whole-body control systems.

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  Build and evaluate algorithms in simulation environments and on real-world humanoid robotic platforms.

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  Contribute to publications and technical reports

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  Assist in mentoring students and supporting research activities

• Master’s degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or related fields

• Strong background in at least one of the following:

  • robot motion planning and control;

  • autonomous decision-making;

  • robot localization and navigation.

• Experience in one or more of the following:

  • path planning, motion planning, trajectory optimization or model predictive control;

  • reinforcement learning, imitation learning or learning-based control;

  • diffusion policies or generative decision-making;

  • robotics physics simulators (e.g., Isaac Gym, MuJoCo, Gazebo)

• Proficiency in Python and/or C++

• Experience with PyTorch or similar deep learning frameworks

• Strong problem-solving ability and hands-on implementation skills

We regret to inform you that only shortlisted candidates will be notified.