The Research Fellow will work directly with Professor Marinko Sarunic's group at UCL Institute of Ophthalmology and UCL Department of Medical Physics and Biomedical Engineering, in addition to Dr Hari Jayaram Consultant Ophthalmic Surgeon and Clinician-Scientist based at Moorfields Eye Hospital NHS Foundation Trust.
The post holder is responsible for delivering key tasks within a large EU grant funded project and will be involved in the development AI supported diagnostic tools and assessing off-the-shelf AI-assisted technology for ophthalmic analysis and data capture, in close collaboration with researchers at Moorfields Eye Hospital.
This Research Fellow post will support key outputs for the Horizon Europe grant awarded project 'TRIO VI CoE (Translational Research and Innovation in Ophthalmology Vision – Centre of Excellence)'. As part of the Horizon Europe 'Widening Participation and Spreading Excellence' actions.
The vacancy is for a highly motivated, creative, and independently thinking Research Fellow with advanced AI specialist skills, with experience of applying these in a clinical ophthalmic setting. The post holder will apply techniques such as machine learning on large datasets to develop prediction models and AI methods to optimise instrumentation used in the diagnosis and treatment of eye disease.
The appointee will form part of a team whose primary research focus is on the development of a Translational Eye Platform (TEP), in the form of Virtual Clinic(s). The TEP aims to accelerate the development and real world deployment of innovative ophthalmic technologies by building the infrastructure, patient engagement processes, expert training, and evaluation systems needed to make TEP fully operational and impactful for both patients and professionals. Duties include:
This post is funded until 31/05/2029 in the first instance. Salary will be on G7, Point 31 (£45,103 per annum inclusive of London Allowance)
The successful candidate will hold an interdisciplinary PhD in applied or medical science spanning digital healthcare, computing and data science, biomedical engineering or a related field.
Extensive knowledge in development and application of AI/machine learning for analysis of medical imaging and data is essential as is a strong understanding of data-based strategies within digital health care.
You will demonstrate evidence of completed research and a suitable publication record including primary / lead authorship in journals relevant to the field of research (AI, machine learning, ophthalmology).
Experience with medical image analysis techniques, with a focus on common ophthalmology modalities (eg CFP, OCT) is also essential.
Application Process: If you have any queries regarding the vacancy please contact Professor Marinko Sarunic (m.sarunic@ucl.ac.uk). If you have any queries regarding the application process please email: ioo.hr@ucl.ac.uk. To apply for the vacancy please click on the 'Apply Now' button below. Please ensure you attach your CV and highest academic qualification. In addition, please provide a personal statement outlining how you meet the criteria to support your application. Please DO NOT attach research papers.
Working with some of the greatest intellects in the world, UCL also offers a generous pension scheme and holiday allowance, plus closure and bank holidays as well as enhanced parental pay and more. Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find out more.
As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.
We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce. These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.
Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality.
Tagged as: Life Sciences
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