This is a funded PhD position in applied medical imaging and deep learning to neurogenerative diseases suited to candidates with an applied mathematics, computer science, electrical engineering, medical imaging, biomedical engineering, physics or equivalent MSc/BSc degree.
We aim to develop artificial intelligence (AI) tools for the automated analyses of optical coherence tomography (OCT) and OCT angiography (OCTA) images for the early detection of dementia. Dementia is detrimental to heathy ageing and exhausts healthcare provision. In the UK alone, the current number of people living with dementia is around 850,000 and the current cost is about £34.7billion and both the number of patients and costs will increase due to the ageing population. There are currently no effective treatments for dementia and thus a diagnosis of an advanced pathological process is difficult to reverse or delay. The successful candidate will develop and validate these new tools with uniquely large cohort data of its kind from Italy and the UK.
The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, high performance computing, mathematics, and medicine. S/he will learn key skills in AI, medical imaging and statistical analysis as well as translational interdisciplinary skills such as healthcare provision and challenges, regulations on medical devices, multidisciplinary team work, IP protection and research ethics. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.
The Institute of Life Course and Medical Science is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.
In addition to the stipend, full UK home tuition fees and research bench fees paid.
The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Computer Science, Mathematics, Statistics, Engineering or Physics. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Python, C++ and Matlab), and signal/image processing plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 or equivalent.
Due to a recent change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.
Enquiries to: Dr Yalin Zheng (firstname.lastname@example.org)
To apply: please send your CV and a covering letter to email@example.com please put Technologies for Healthy Ageing in the subject line
Expected interviews in April 2021
Supervisors: Dr Yalin Zheng, Prof Y Shen, Dr Antonella Macerollo, Dr Vito Romano and Dr Rodolfo Sardone
Open to students worldwide
The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.
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5. Czakó C, Kovács T, Ungvari Z, Csiszar A, Yabluchanskiy A, Conley S, et al. Retinal biomarkers for Alzheimer’s disease and vascular cognitive impairment and dementia (VCID): implication for early diagnosis and prognosis. GeroScience. 2020;42(6):1499-525.