Master's Studentship: Computer Vision for Intelligent Visual Inspection at Council for Scientific and Industrial Research (CSIR)

Research areas:

• Foundation models for defect segmentation and anomaly detection (e.g. SAM, SAM2, Vision Transformers)
• Domain adaptation and transfer learning for industrial inspection tasks
• Explainability and interpretability methods for AI-based inspection systems
• Dataset preparation, annotation workflows, and model benchmarking for defect detection
• Real-time deployment of segmentation models for industrial computer vision
• Multimodal data integration and AI-assisted quality control systems

Key responsibilities:

• Prepare research proposal to be approved by both the CSIR and the university;
• Prepare a written literature overview of the current state-of-the-art around the research topic;
• Perform original research to solve the open research problem that the student identified in his/her proposal;
• Produce sufficient quantity and quality of peer-reviewed publications around this research topic;
• Compile the master's thesis and defend the work successfully;
• Contribute to extra activities that may be outside the scope of the master's research as per the needs of the research group. The amount of such work shall be limited to 20% of the total time.

Qualifications, skills and experience:

• An Honours degree in computer science/engineering, electrical/electronic engineering, information technology or a related field;
• The ability to generate research questions and hypotheses: Review, critique and synthesize a body of research, identifying significant gaps in knowledge, methods, and study subjects to develop research questions and testable hypotheses;
• Effectively communicate issues, research findings and implications of research verbally and in writing to appropriate professional and public audiences;
• Enthusiastic and self-motivated individuals who take ownership and initiative and have the willingness to learn;
• The ability to work well under pressure as part of a team and as well as independently 
• Foundational knowledge of machine learning, deep learning, and computer vision; familiarity with Python and common ML frameworks (e.g. PyTorch, TensorFlow) will be advantageous;
• A good peer-reviewed publication record will be advantageous.