Research Fellow, SORC

The SNEC Ocular Reading Centre is seeking candidates who are highly motivated to join us for a fulfilling career as a Research Fellow. The incumbent (0.5FTE) will be required to develop and implement advanced AI models and software solutions for automated segmentation and visualisation of ocular diseases from multimodal retinal imaging. This role supports the Centre's clinical research objectives by creating robust, clinically-validated tools that enhance diagnostic accuracy, enable disease progression monitoring, and facilitate evidence-based treatment decisions. The incumbent will bridge the gap between cutting-edge AI research and practical clinical applications, ensuring developed solutions meet regulatory standards and integrate seamlessly into clinical workflows to improve patient outcomes and advance ophthalmic care.

The other 0.5 FTE will be supported by SERI to work on research projects assigned by PI.

The Research Fellow will be required to work on:

• AI Model Development: Develop deep learning models for ocular disease segmentation from retinal imaging, create visualisation tools for clinical interpretation of AI-generated results, implement validation frameworks for model performance assessment

• Clinical Validation and Quality Assurance: collaborate with clinicians and other healthcare staff on model validation and performance assessment, conduct external validation studies across different patient populations, monitor model performance and implement quality control measures

• Software Development and Clinical Support: build software solutions suitable for clinical research and operational environments, provide technical support for AI-enabled clinical workflows, implement data governance protocols and quality control systems

• Other duties and roles as assigned by the team/department

Job Requirements:

• PhD in Computer Science, Biomedical Engineering, or related field with focus on medical AI

• Demonstrated expertise in deep learning frameworks (PyTorch preferred) and computer vision

• Strong background in clinical AI validation methodologies and regulatory considerations

• Experience with retinal imaging analysis and ophthalmic disease understanding

• Proficiency in Python, C++, and software engineering best practices

• Track record of publications in peer-reviewed journals and conference proceedings

We would preferred candidates with:

• Previous experience in ophthalmology research or clinical AI deployment

• Knowledge of foundation models and AI/LLM learning approaches

• Experience with multi-modal medical imaging and data harmonisation

• Understanding of clinical trial design and medical device regulation