Job Description

A Research Fellow position is open in a research lab at the Department of Electrical and Computer Engineering, National University of Singapore (NUS).

The Research Fellow will work on autonomous drone swarm navigation in unknown and GNSS-denied environments, with a focus on developing scalable, robust, and distributed multi-agent autonomy algorithms. The project aims to enable drone swarms to operate in cluttered, partially observable, and dynamically changing environments, such as indoor spaces or disaster-response scenarios.

The Research Fellow will develop distributed perception, planning, and control algorithms for multi-UAV systems, including mapless navigation, cooperative exploration, and collision avoidance under limited communication. The work will integrate model-based approaches (e.g., optimization-based control, graph-based coordination, control barrier functions) with learning-based methods (e.g., reinforcement learning, imitation learning, or diffusion-based policies) to achieve both theoretical guarantees and strong empirical performance.

The candidate will be responsible for both algorithmic development and system-level implementation, including deployment on real drone swarms. This includes simulation (e.g., IsaacSim, PX4 SITL), onboard computing, multi-agent communication, and hardware experiments.

In addition, the Research Fellow is expected to contribute to mentoring graduate students and leading research directions within the project.

The initial appointment duration is 12 months, extendable based on performance.

The candidate should have a Ph.D. degree from a reputable university, with expertise in UAV, robotics, multi-agent systems, and control.

A successful candidate should have strong practical skills in quadrotor platforms and full-stack autonomous navigation algorithms.

Qualifications

•    Possess a Ph.D. degree in Electrical Engineering, Computer Science, Robotics, Aerospace, or related disciplines. 
•    Strong hands-on experience in quadrotor platforms and robotic navigation
•    Family with LIO/VIO, robotic exploration and navigation, motion planning and trajectory optimization, mapless navigation 
•    Proficient in C++ or Python; experience with ROS and simulation tools (e.g., Gazebo, IsaacSim). 
•    Experience with multi-robot communication systems (e.g., ad hoc wireless networks) is a plus. 
•    Demonstrated ability to conduct both theoretical and experimental research. 
•    Excellent communication skills and ability to publish independently. 
•    Experience supervising students or leading projects is preferred.
•    Open to Fixed Term Contract