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PhD Studentship - Image Guided Intervention Supported with mixed Reality

University of Leeds - Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2025/26 - Computer Science

Qualification Type: PhD
Location: Leeds
Funding for: UK Students, EU Students, International Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 12th May 2025
Closes: 31st July 2025

Project Link: Image guided intervention supported with mixed reality | Project Opportunities | PhD | University of Leeds [4ff9-7c9a-edd-cc94]

Project Title: Image guided intervention supported with mixed reality

Funding: School of Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. 

Lead Supervisor’s full name & email address

Dr. Sharib Ali: s.s.ali@leeds.ac.uk

Co-supervisor’s full name & email address

Dr. Rafael Kuffner dos Anjos: r.kuffnerdosanjos@leeds.ac.uk

Dr. Shahid Farid (Clinical – External)

Project summary

The PhD studentship will broadly explore development of cutting-edge AI solutions for image registration, anatomy segmentation, and immersive technology. The selected candidate will work with preoperative MRI/CT scans to create 3D models of organs and intra-operative surgical videos. The candidate will develop surgical planning and implement new technologies for its integration as VR/AR assistive platform. The research directions will be adapted based on the progress and consultation with clinical colleagues and supervisor, ensuring impactful outputs. The candidate will have opportunity to collaboratively work with computational scientists and surgeons exploring novel ways to transform research and practice in surgical care.

Please feel free to contact the main supervisor informally with your CV and a short list of interests for a discussion.

Please state your entry requirements plus any necessary or desired background

A first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics.

Subject area: Medical imaging, biomedical engineering, computer science & IT

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