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Offer: PhD Scholarship in Weakly-Supervised Machine Learning for Medical Image Analysis:Job description:Qualification Type: PhD Modern deep learning techniques achieve human-like performance in many medical image analysis tasks, including the identification of anomalous tissue/pathology from medical scans. To be trained, these techniques typically require large image datasets with pixel-level annotations provided by medical experts. However, obtaining reliable annotations is very difficult (due to the intrinsic nature of the task, especially for rare/complex pathologies) and highly time-consuming. This severely hinders the development and deployment of AI into clinical practice, despite its huge potential. This PhD Scholarship will focus on designing novel approaches that require less detailed/reliable annotations but are still capable of producing highly accurate results. These approaches will include training models through weak supervision (i.e. leveraging only coarse annotations provided by the experts) and incorporating noise-robust learning strategies (i.e. accounting for the presence of unreliable annotations). We expect that many high-impact publications will be generated during the project, to be presented both in computer science-related venues (e.g. CVPR, NeurIPS, MICCAI) as well as at medical conferences (e.g. ISMRM, ESMRMB). The PhD candidate will work in an exciting international environment in the heart of the City of London. They will join the School of Science and Technology at City, University of London (member of the Alan Turing University Network) and the CitAI Research Centre (which features academic staff with broad skills in machine learning for healthcare). They will also be able to exploit the power of Hyperion, City's High-Performance Computer. This Scholarship will be carried out in collaboration with St George's, University of London (which is merging with City University). The candidate will have access to St George's highly valuable clinical datasets (e.g. MRI of patients with brain tumours, brain injury, diseases of aging) as well as supervision from leading biomedical researchers with strong links to radiology. Consequently, the research outputs of this Scholarship will have potential for impact in clinical practice. What is offered: The Scholarship includes: » A competitive annual bursary for 3 years (£21,000/year) » Full tuition fees for UK/Home Students. Partial fee coverage for European/Overseas Students » The opportunity to gain up to £4,300/year through a non-compulsory teaching adjunctship » Over £4000 to participate to conferences and training Eligibility: The studentships will be awarded located on outstanding academic achievement and the potential to produce cutting-edge research. Prospective candidates must: » Hold a good honours degree (no less than a second-class honours degree or an equivalent qualification) in an appropriate subject » expertise of modern machine learning techniques for computer vision and practice with coding in Python is beneficial (but not a strong requirement) » candidates whose mother tongue is not English must meet any one or a combination of the following: » A minimum IELTS average score of 6.5; with a minimum of 6.0 in each of the four components » The award of a Masters' degree, the teaching of which was in English from an English-Speaking Country For questions regarding the application process, Contact Us by pgr.sst.enquire@city.ac.uk. For questions regarding the project, Contact Us by the academic manager (Dr Giacomo Tarroni, giacomo.tarroni@city.ac.uk). How to apply: To apply online, please click the 'Apply' button, above. No project proposal is required: simply upload a document with the title of the Scholarship. Closing date: 30thof Jun 2024or until the position has been filled. The prosperous candidate will ideally start his/her doctorate in Jul 2024 (but a later date can be considered) . Skills:
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