Internship - AI Image Processing & Modelling

The student will be responsible for expanding AI models, building image datasets, and improving model performance through testing and optimisation activities.

Your Role

Key responsibilities in your new role

• Deep Learning Model Expansion: Support the expansion of Deep Learning AI models for Image Segmentation, Classification, and Quantification use cases with the latest production data.
• Deep Learning Dataset Expansion: Expand, curate, and maintain image datasets for model training and validation.
• Data Labelling: Perform image annotation and data labelling to support supervised learning workflows.
• Image Augmentation: Develop image synthesis and augmentation pipelines to improve dataset diversity with newer data.
• Model Testing: Evaluate model performance using appropriate metrics and validation methodologies; perform model testing, benchmarking, and optimisation to improve accuracy, reliability, and inference efficiency.

Internship Learning Outcomes:

• Applied Deep Learning: Gain hands-on experience building and optimising AI Computer Vision solutions for real industrial applications.
• Model Development End-to-End: Gain exposure to the full AI development lifecycle and application of deep learning in Engineering use cases.
• Real World Use Cases: Contribute to impactful digitalisation initiatives and strengthen your portfolio in AI and Data Science.

Your Profile

Qualifications and skills to help you succeed

• Educational Background & Discipline: Currently pursuing a Bachelor's degree in Computer Engineering, Data Science, Computer Science, Artificial Intelligence, Electrical & Electronic Engineering, or a related technical discipline with strong aptitude and interest in Data Science, Machine Learning, and Artificial Intelligence.
• AI Proficiency: Familiarity with the AI/ML model development lifecycle, including data preparation, model training, validation, deployment, dataset annotation, image labelling techniques, and optimisation.
• Image Processing Skillset: Solid knowledge of Computer Vision and Image Processing concepts.
• Programming Proficiency: Proficiency in Python programming with familiarity in popular image processing and AI libraries such as OpenCV, PyTorch, and TensorFlow.
• AI Model Exposure: Exposure to Deep Learning architectures such as CNNs, U-Net, EfficientNet, or related Computer Vision models and image use cases including classification, segmentation, and detection.
• Image Augmentation Understanding: Knowledge of data augmentation, synthetic data generation, or Generative AI techniques is a plus.

Contact:
    Hillary Woo

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