- Design and manage the field data collection pipeline — including body-worn cameras, fixed-position gesture capture cameras, and pole-mounted lane closure cameras deployed at active road worksites

- Annotate, label, and structure video datasets capturing vehicle behaviour, ASO gestures, and traffic compliance events

- Train and optimise computer vision models (YOLOv8 or equivalent) for vehicle detection, trajectory tracking, and driver compliance classification in outdoor road worksite environments

- Calculate traffic safety metrics including Post-Encroachment Time (PET) to identify and prioritise near-miss events for model training

- Build and refine logit or ML-based conflict severity models to support edge case identification in the training dataset

- Export trained models to TensorRT and deploy on NVIDIA Jetson Orin NX hardware

- Develop ROS2 integration for deploying the perception pipeline onto a humanoid robot platform

- Build multi-robot coordination logic using ROS2 pub/sub architecture for coordinated deployment scenarios

- Support gesture recognition model development using captured ASO hand-signal datasets

Requirements

- Degree in Computer Science, Electrical Engineering, or a related field

- Hands-on experience with YOLOv5 / YOLOv8 model training and deployment

- Experience with object tracking (vehicle or pedestrian trajectory analysis)

- Familiarity with traffic safety metrics (PET, conflict analysis) is a strong advantage

- Python proficiency; experience with PyTorch and TensorRT preferred

- Experience or strong interest in ROS2 and embedded deployment (Jetson platform) preferred

- Ability to work in outdoor environments at road worksite locations

- Self-directed and comfortable working on long-horizon R&D projects