Research Engineer/Fellow (Deep Learning Computer Vision - SHNeo)

• Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables.

• Undertake the following specific responsibilities in the project:
• Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets.
• Design and implement software modules to integrate the models into a working system prototype.
• Perform data annotation.
• Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency.
• Prepare project documentation, technical reports, and academic publications.
• Collaborate with industry partners and contribute to technology transfer efforts.

• Deep learning frameworks (e.g., PyTorch, TensorFlow)
• Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN)
• Computer vision techniques and algorithms
• Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models

• A Master's or PhD degree in relevant areas will be advantageous.

• Participation in Kaggle competitions, showcasing practical problem-solving and model development skills
• Model deployment (e.g., ONNX, TensorRT)
• Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano)
• Real-time processing and GPU acceleration
• Experience working on industry R&D projects

• Able to build and maintain strong working relationships with team members, stakeholders, and external partners
• Self-motivated and committed to continuous learning and improvement
• Proficient in technical writing & presentation, research reporting, and academic publication
• Possess strong analytical, problem-solving, and critical thinking skills
• Demonstrate initiative and ownership in carrying out tasks independently

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Singapore Institute of Technology