Imaging techniques (e.g. optical, PET, MRI, CT, X-rays) play an ever-increasing role in healthcare. A large amount of imaging, clinical and demographic data in particular has been continuously generated in healthcare. The potentials of these big data have not been fully exploited. The recent advances in image processing and artificial intelligence have made it feasible for the computer to be trained to aid the diagnosis and prediction of various diseases such as eye disease. This project aims to develop new methodologies in the field of image processing (e.g. image enhancement, segmentation and registration) and artificial intelligence (e.g. deep learning) that support automated and accurate diagnosis and prediction in medical images.
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. 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 Ageing and Chronic Disease 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. The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. We offer a supportive working environment with flexible family support for all our staff and students and applications for part-time study are encouraged. 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.
The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Mathematics, Engineering, Physics or Computer Science. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Matlab, Python or C++), 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.
The successful applicant will be expected to provide the funding for tuition fees, bench fees of approximately £3,000 per year and all living expenses. Details of the cost of study can be found on the University website. There is NO funding attached to this project.
We have a thriving international researcher community and encourage applications from students of any nationality able to fund their own studies (Government scholarship), or who wish to apply for their own funding (e.g. China Scholarship Council).
1. Albarrak A, Coenen F, Zheng Y. Computerized Medical Imaging and Graphics. 2017; 55: 113-123.
2. Zhao Y, Zheng Y, Liu Y, Yang Y, Zhao Y, Chen D, Wang Y. IEEE Trans Med Img. 2016;36:51-63.
3. Zhao Y, Rada L, Chen K, Harding S, Zheng Y. IEEE Trans Med Img. 2015;34:1797-807.
4. LeCun Y, Bengio Y, Hinton G. Nature. 2015;521:436-444.
How good is research at University of Liverpool in Clinical Medicine?
(joint submission with Liverpool School of Tropical Medicine)
FTE Category A staff submitted: 143.50
Research output data provided by the Research Excellence Framework (REF)
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