Ref Number B02-08763 Professional Expertise Research and Research Support Department School of Life & Medical Sciences (B02) Location London Working Pattern Full time Salary £43,374 £51,860 Contract Type Fixed-term Working Type Hybrid Available for Secondment No Closing Date 01-Jul-2025 About Us The evidENT team, which is part of UCL's Ear Institute applies artificial intelligence to address challenges in hearing healthcare. We are a multidisciplinary group of clinicians, data scientists, and researchers working on medical image analysis, machine learning, and audiology. Our recent work has focused on using deep learning to analyse temporal bone CT scans and brain MRI data in patients with hearing disorders. You will join a collaborative environment where your technical expertise in computer vision will contribute to clinical applications. Our team are now expanding our work to develop automated segmentation tools and diagnostic aids for otologic conditions. Working with ENT surgeons, audiologists, and AI researchers, you'll have access to datasets including audiograms and corresponding imaging data. Our partnership with the UCLH Biomedical Research Centre and NIHR Hearing Health Informatics Collaborative provides a strong foundation for translational research. We value technical excellence and a practical approach to improving patient care. If you're interested in applying computer vision techniques to address clinical challenges in hearing health, our team offers the opportunity to work on meaningful projects with direct clinical relevance. About The Role In this role, you will develop and implement computer vision and deep learning algorithms to analyse CT and MRI data from patients with hearing disorders. You will focus on creating automated segmentation methods for temporal bone structures, designing predictive models for treatment outcomes, and developing diagnostic tools for conditions such as otosclerosis. The position requires expertise in medical image analysis, proficiency with neural network architectures (particularly CNNs for segmentation tasks), and experience processing 3D medical imaging data. You will collaborate closely with clinicians to ensure your technical work addresses practical clinical needs, while contributing to publications, grant applications, and the development of open-source tools for the hearing healthcare community. This is a fixed-term role until 01 September 2027 in the first instance. This role is eligible for hybrid working with a minimum of 80% on site. This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit https://www.ucl.ac.uk/human-resources/conditions-service-research-teaching-and-professional-services-staff for more information. The advert will close on 01 July 2025 at 23:59 GMT, however we may close applications early if we receive a high volume of applications. Early application submission is recommended. Interviews are scheduled for: 15 July 2025 If you have any queries about the role, please contact nischay.mehta@ucl.ac.uk. If you need reasonable adjustments or a more accessible format to apply for this job online or have any queries about the application process, please contact Ear Institute HR at ear.hr@ucl.ac.uk The UCL Ways of Working supports colleagues to be successful and happy at UCL through sharing expectations around how we work – please visit www.ucl.ac.uk/ways-of-working to find out more. In the event we get a high number of applications, we may close the advert early before the published closing date. As a minimum we will keep all adverts open for 2 weeks About You You should have a PhD in computer science, biomedical engineering, or a related field with strong expertise in medical image analysis and deep learning. You are proficient in implementing and optimising convolutional neural networks for medical image segmentation, with demonstrated experience working with 3D imaging data such as CT or MRI scans. Your technical skills include Python programming and familiarity with deep learning frameworks (PyTorch or TensorFlow), along with a solid understanding of image processing techniques for medical applications. You have a track record of successfully developing computer vision solutions for challenging segmentation tasks, ideally with experience in handling small anatomical structures or working with limited training data. You can effectively communicate complex technical concepts to clinical collaborators and can work independently while contributing to the broader research goals of the team. What we offer As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below: • 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days) • Additional 5 days’ annual leave purchase scheme • Defined benefit career average revalued earnings pension scheme (CARE) • Cycle to work scheme and season ticket loan • Immigration loan • Relocation scheme for certain posts • On-Site nursery • On-site gym • Enhanced maternity, paternity and adoption pay • Employee assistance programme: Staff Support Service • Discounted medical insurance • Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefitsto find out more. Our commitment to Equality, Diversity and Inclusion 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. You can read more about our commitment to Equality, Diversity and Inclusion here : https://www.ucl.ac.uk/equality-diversity-inclusion/ Available documents Attached documents are available under links. Clicking a document link will initialize its download. • download: PostDoctoral Research Fellow in Artificial Intelligence\_May 2025.pdf