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Project Overview

The University of British Columbia (UBC) is seeking a highly motivated and skilled Postdoctoral Fellow to join our R&D team for a groundbreaking applied AI project focused on developing a next-generation aviation safety system. This position will contribute to a collaborative, interdisciplinary initiative supported by industry and academic partnerships. The successful candidate will lead end-to-end model development, system and architecture design, and performance benchmarking in a lab-based research environment. This is a one-year full-time appointment with the possibility of extension, based on performance and project progression. The postdoc will play a central role in designing and refining perception models, integrating them into the data pipeline, and evaluating system behavior under real and simulated scenarios.

Key Responsibility

• Design, train, and evaluate AI models for object detection, classification, and tracking using aerial imagery
• Develop, manage, and process datasets for model training and testing, including synthetic scene generation
• Optimize model architecture and performance based on benchmarking
• Contribute to the model deployment and end-to-end system design
• Conduct rigorous system validation through controlled bench testing
• Collaborate with engineers and the team lead to ensure system alignment with stakeholder goals
• Communicate results through technical reports, presentations, and collaborative meetings

Qualifications

• D. in Computer Science, Electrical Engineering, Aerospace Engineering, or related field
• Demonstrated experience in training and deploying deep learning models (e.g., CNNs, transformers)
• Strong programming skills in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
• Familiarity with data annotation, synthetic data tools (e.g., Unreal Engine, Blender), and ML workflows
• Experience working with or developing ML pipelines, preferably in Kubernetes or cloud-based environments
• Strong problem-solving, communication, and project management skills
• Ability to work independently and collaboratively in a multidisciplinary team

Preferred Experience

• Experience with data visualization tools, MLflow, and data versioning tools
• Prior work with aviation, remote sensing, or safety-critical systems
• Familiarity with uncertainty modeling or sensor fusion

Benefits

• Competitive salary and potential for contract renewal
• Mentorship and collaboration opportunities with leading AI engineers and academic researchers
• Opportunity to work on impactful, safety-critical technology with real-world applications

Professional Development Opportunities

This position offers exceptional opportunities for career advancement in applied AI research, providing hands-on experience in translating academic research into practical safety-critical systems. The fellow will gain valuable exposure to industry collaboration, regulatory considerations, and the commercialization of AI technologies in high-stakes environments. The project aligns with national priorities in AI innovation and transportation safety, positioning the fellow at the forefront of emerging technologies that directly impact public safety and operational efficiency.

Application Requirements

• Curriculum vitae with publication list
• Cover letter describing research interests and relevant experience
• Names of three academic or professional references
• Two representative publications or technical reports

Review of applications will begin immediately and continue until the position is filled.