Job Description:
- Design, implement and optimize algorithms (e.g. SLAM) for real-time, high-precision localization of autonomous vehicles using sensor fusion of GNSS/IMU/DR, LiDAR, cameras, HD Map, and other sensors.
- Develop and maintain high-definition (HD) maps for autonomous driving applications. Work on the creation, updating, and management of detailed maps that include lane information, road geometry, traffic lights/signs, and other relevant features.
- Improve perception models for feature detection and tracking. Use deep learning and computer vision techniques to detect key features such as road boundaries, obstacles, and dynamic objects to enhance mapping and localization accuracy.
- Develop test plans, perform validation, and run simulations and testing to ensure the accuracy, reliability, and safety of mapping and localization systems in both controlled and real-world environments.
- Stay up to date with the latest advancements in localization, mapping, and autonomous driving technologies. Lead efforts to continuously improve the performance and capabilities of our mapping and localization systems.
- Write professional design and technical documentation.
Job Requirements:
- Master’s or Ph.D. degree in Robotics, Computer Science, Electrical and Electronic Engineering, Computer Vision, Deep Learning and AI, or a related field.
- Working experience in localization, mapping, or autonomous driving systems is preferred.
- Expertise in key mapping and localization techniques including GNSS/IMU/INS, SLAM, and especially deep learning-based perception and sensor fusion.
- Strong analytical and problem-solving skills, strong programming skills in Linux C/C++, excellent communication skills, ability to work collaboratively with the team, etc.
The above information on this description has been designed to indicate the general nature, and level, of the work performed by this position. It is not designed to contain, or be interpreted, as a comprehensive inventory of all duties, responsibilities and qualifications required.
Interested candidates may also apply or write in to CS Chua, EA 12C6130/ R1548619 [email protected]. Shortlisted candidates will be contacted for a discussion.