Postdoctoral Fellow: Multi-Camera Spatio-Temporal Vehicle Understanding | EAIGLE × University of Waterloo

EAIGLE, in collaboration with the University of Waterloo, is hiring a postdoctoral fellow to research models that track and understand transport vehicles across multiple cameras and over time. The fellow will develop and validate computer vision and machine learning models, publish research, and collaborate with academic and industry teams to translate findings into deployed solutions.

Responsibilities:

• Spatio-temporal tracking of vehicles across multiple cameras and extended time periods
• Vehicle text recognition: license plates, trailer and container IDs, and other markings
• Vehicle type classification: tractors, trailers, tankers, containers and chassis
• Physical condition assessment: damage, doors, seals and changes over time
• Reasoning under uncertainty: fusing evidence across cameras and time into a consistent, confidence-aware picture of each vehicle
• The fellow will develop and validate computer vision and machine learning models using large volumes of real-world data from EAIGLE deployments, along with synthetic data from EAIGLE’s simulation pipelines
• They will run experiments, publish at top academic venues, and work with EAIGLE’s engineering and product teams to turn research into deployed solutions
• The fellow will be supervised by faculty at the University of Waterloo, present findings to academic and industry stakeholders, mentor graduate students, and help shape future research directions and grant proposals