MRC DiMeN Doctoral Training Partnership: Applying Machine Learning to 3D single molecule localisation analysis in super-resolution microscopy

Prof M Peckham
Dr J Leng
Monday, May 14, 2018
Competition Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description
Funding Notes
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References

Project Description

Super-resolution imaging is an advanced form of microscopy that allows a 10 fold improvement in the resolution that can be obtained compared to confocal light microscopy. To obtain this improvement in resolution, fluorescently labelled molecules are driven to ‘blink’ stochastically, such that only a small number of molecules are fluorescent at any one time, and their positions accurately determined. As a model system, we will image the organisation of proteins in Z-discs of cardiomyocytes (100nm wide) in normal and diseased cells.
MRC remit: This technique provides very detailed images on protein organisation within cells and is applicable to almost all of the areas of biology that MRC supports. It also fits with enabling new ways of working, in that this area of research is underpinning and driving new technological approaches.

Funding Notes

This studentship is part of the MRC Discovery Medicine North (DiMeN) partnership and is funded for 3.5 years. Including the following financial support:
Tax-free maintenance grant at the national UK Research Council rate
Full payment of tuition fees at the standard UK/EU rate
Research training support grant (RTSG)
Travel allowance for attendance at UK and international meetings
Opportunity to apply for Flexible Funds for further training and development
Please carefully read eligibility requirements and how to apply on our website, then use the link on this page to submit an application: View Website

References

Elife, 6 (2017); Nature Cell Biology, 18, 122-31 (2016); Methods, 88, 37-47 (2015)

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MRC DiMeN Doctoral Training Partnership: Applying Machine Learning to 3D single molecule localisation analysis in super-resolution microscopy

University of Leeds