Research Fellow (VIO/LIO & State Estimation)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28VIOLIO-&-State-Estimation%29/32577-en_GB/

We regret that only shortlisted candidates will be notified.

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 closely with the Principal Investigator (PI) on the project Meta-Learning of Robust State Estimation for Agile Mobile Robots. The position focuses on advancing state estimation and odometry for robotic systems, with particular emphasis on LiDAR-Inertial Odometry (LIO), Visual-Inertial Odometry (VIO), and multi-sensor fusion for UAVs and other agile platforms.

The Research Fellow will develop high-performance, robust estimation algorithms that operate reliably in challenging environments (e.g., low texture, illumination variation, dynamic scenes, or sensor degradation). The research will integrate model-based estimation methods (e.g., filtering, factor graph optimization, moving horizon estimation) with learning-enhanced components, including meta-learning approaches for adaptive and generalizable estimation.

The candidate will be responsible for both theoretical development and system-level implementation, including real-time deployment on robotic platforms. The developed algorithms will be validated through simulation and real-world experiments, particularly on UAV systems.

In addition, the Research Fellow is expected to contribute to mentoring junior PhD students and supporting research activities within the project.

The initial appointment duration is 12 months, which can then be extended based on an evaluation at the end of the initial appointment.

The candidate should have a Ph.D. degree from a reputable university, with expertise in robotic state estimation, SLAM, or perception for autonomous systems.

A successful candidate should have a strong mathematical background (e.g., linear algebra, optimization, probability, stochastic processes), and a strong publication record in top-tier venues.

Qualifications

. Possess a Ph.D. degree in Electrical Engineering or related disciplines (e.g., Computer Science, Robotics, Mechanical Engineering, Aerospace, Mathematics).
. Have substantial research experience in robotic state estimation, SLAM, or odometry systems.
. Strong expertise in LiDAR-Inertial Odometry (LIO), Visual-Inertial Odometry (VIO), or multi-sensor fusion.
. Solid understanding of estimation and optimization frameworks, including EKF/UKF, factor graph optimization, bundle adjustment, or moving horizon estimation (MHE).
. Experience with real-time perception systems and deployment on robotic platforms.
. Proficient in C++ or Python experience with ROS and robotic middleware.
. Familiar with UAV systems (e.g., PX4), and simulation environments such as Gazebo or AirSim.
. Experience with sensor calibration, time synchronization, and system integration is highly desirable.
. Strong publication record in leading journals and conferences in robotics, control, or machine learning.
. Excellent communication skills and ability to independently publish and present research outcomes.
. Demonstrated ability to supervise students or contribute to research leadership is preferred.
. Experience in world-class research environments is highly valued.
. Open to Fixed Term Contract