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Linking eye movements with predictive algorithms for high resolution retinal imaging in the living human eye


   Biosciences Institute

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  Dr Laura Young, Dr Tim Morris, Dr Boguslaw Obara  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Recent technological developments have allowed us to image the back of the eye with such high resolution that we can see the individual light-sensitive cells responsible for sight in a living human. This extraordinary ability to study the living eye in microscopic detail is helping us to understand what constitutes a healthy retina and how this changes with age. It allows us to probe the visual system on a cellular level to understand the basic building blocks of sight. 

Capture and analysis of biological images can be challenging and this is particularly true when the ‘sample’ is a living, moving system such as the eye. But, can we turn this to our advantage? The movements of the eye are not random, they follow characteristic patterns of movement and actively extract relevant information from a visual stimulus. Can we use our understanding of how the eyes move to develop better techniques for capturing and analysing high resolution retinal images?

This project uniquely combines research into the human visual system with novel optical correction and image processing approaches, using sophisticated instrumentation. It will appeal to applicants with a background in physics or computer science and an interest in biological or biomedical imaging, particularly in vivo retinal imaging, and/or visual neuroscience. Interdisciplinary training will be provided in imaging instrumentation and analysis techniques (including machine learning), and visual psychophysical experimentation. This is a fantastic opportunity to develop a broad but complementary skillset and explore interdisciplinary research in an emerging field.

Informal enquiries may be made to the primary supervisor: [Email Address Removed].

The studentship should be commenced before the end of 2022.

HOW TO APPLY

Applications should be made by emailing [Email Address Removed] with:

·      a CV (including contact details of at least two academic (or other relevant) referees);

·       a covering letter – clearly stating your first choice project, and optionally 2nd ranked project, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University;

·      copies of your relevant undergraduate degree transcripts and certificates;

·      a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE AT https://www.nld-dtp.org.uk/how-apply. Applications not meeting these criteria may be rejected.

In addition to the above items, please email a completed copy of the Additional Details Form (as a Word document) to [Email Address Removed]. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.

The closing date for applications is Friday 8th July 2022 at 12noon (UK time).


Funding Notes

Studentships are funded by the Biotechnology and Biological Sciences Research Council (BBSRC) for 4 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend. We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. Note that home (UK) candidates may also apply to this studentship.

References

ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods, Scientific Reports, 11, 11225, 2021.
What makes a microsaccade? A review of 70 years research prompts a new detection method, Journal of Eye Movement Research, 12(6), 2020.
Compact, modular and in-plane AOSLO for high-resolution retinal imaging, Biomedical Optics Express, 9(9), 4275-93, 2018.
Predicting post-operative vision for macular hole with automated image analysis. Ophthalmology Retina, in press., 2020.
Wavefront prediction using artificial neural networks for open-loop adaptive optics. Monthly Notices of the Royal Astronomical Society, 496(1), 456-464, 2020.
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