Job Description:
A leading name in the education sector is seeking an experienced AI / Machine Learning Researcher to work on impactful projects at the intersection of cutting-edge technology and transportation safety.
Key Responsibilities:
- Investigate, benchmark, and optimise model architectures for detection, segmentation, and tracking in complex transport environments.
- Enhance multi-object tracking pipelines using advanced methods such as DeepSORT, ByteTrack, and Transformer-based approaches.
- Improve video analytics performance through evaluation of alternative models and pipeline optimisation.
- Develop post-processing logic for trajectory denoising, smoothing, and error reduction.
- Apply denoising techniques to improve low-resolution or low-bitrate footage.
- Conduct field validation of model performance across diverse environments and camera types.
- Support camera calibration workflows for accurate spatial measurements.
- Run ablation studies and model evaluations under varied environmental conditions.
- Collaborate with engineering teams for scalable inference deployment using ONNX, TensorRT, Triton, or similar.
- Translate AI outputs into transport safety and engineering metrics.
- Contribute to documentation, reports, and publications.
Essential Criteria:
- PhD in Computer Science, Applied Mathematics, Robotics, Physics, or related discipline; or equivalent experience with a strong applied AI/computer vision track record.
- Expertise in object detection, segmentation, and multi-object tracking in complex scenarios.
- Proficiency with deep learning frameworks (PyTorch, TensorFlow) and Python libraries (OpenCV, Yolo, NumPy, scikit-learn).
- Proven experience in real-world video analytics and model deployment under challenging conditions.
- Experience with model optimisation and inference acceleration.
- Familiarity with denoising techniques for low-quality video.
- Understanding of camera calibration and spatial measurement.
- Strong communication skills and ability to work independently and in multi-disciplinary teams.
Desirable:
- GPU-based inference deployment (ONNX, TensorRT, Triton).
- Knowledge of transport/traffic datasets.
- Track record of publications or open-source contributions.
- MLOps tools and practices (MLflow, Weights & Biases).
- Experience with Transformer architectures for video data.