Advanced Microscopy Image Analysis for Life Science Applications

Description

Microscope imaging plays an ever-increasing role in life science research. A large number of images can be continuously generated by various different devices (light microscopy, fluorescence microscopy, electron microscopy, and light-sheet microscopy). The potential of this big data has 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 scientists to address fundamental questions in biology. 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) to support life applications.

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.

Any specific eligibility requirements: 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.

Please note this position will remain open until a suitable candidate is found.

Informal enquiries regarding this project should be made to Dr Yalin Zheng (). All general enquiries should be directed to Mrs Sue Jones ().  

To apply please send your CV and a covering letter to Dr Zheng () with a copy to

Availability

Open to students worldwide

Funding information

Self-funded project

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). 

Supervisors

References

1. Kervrann C, Sorzano C, Acton S, Olivo-Marin J, Unser M. IEEE Journal of Selected Topics in Signal Processing. 2016; 10: 6-30. 


2. LeCun Y, Bengio Y, Hinton G. Nature. 2015;521:436-444.

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