MRC competition funded project available: “Accelerating super-resolution microscopy with machine learning and pattern recognition”

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https://www.findaphd.com/search/ProjectDetails.aspx?PJID=93052

A new 3.5-year PhD opportunity is now open in our group to incorporate Artificial Intelligence and Pattern Recognition towards accelerating one of the most exciting Biomedical imaging technologies available today.

Single molecule localisation microscopy (SMLM) is an invaluable tool for visualising molecular scale interactions and structures which are fundamental to life at the sub-cellular level. Super resolution images are assembled laboriously in SMLM experiments molecule by molecule; therefore it is a time intensive imaging modality which requires up to 90 mins to generate each image. These constraints have been a major barrier to its utility in the Medical Sciences. For example, the slow acquisition speed has made it undesirable for realtime live cell imaging experiments. In the proposed project, we will build a stand-alone programme which can reconstruct the fine spatial features of super-resolution images in a mere fraction of the time needed for recording a full image with a standard protocol. The programme will achieve this by combining existing single molecule localisation algorithms implemented in Python and pattern-determination tools which we have developed to predict the final image iteratively. The software will use an iterative error estimation and image correction algorithm which will feed into a machine learning (ML) platform built-in with Python to inform the process of pattern determination. As the software learns iteratively, to perform pattern determination faster and more robustly, the time required to reconstruct the image using a live stream of primary image data is expected to be abbreviated significantly. This unprecedented level of speed will unlock a wide range of innovative and complex experiments as well as novel technologies which have not been possible thus far. For example, the added speed will enable molecular-scale mapping of rapid cellular events (in the order of seconds) in living disease models.

The successful candidate will work with Dr I.Jayasinghe (www.musclesuperres.com; Twitter @i_jayas), Prof Nikita Gamper (https://www.fbs.leeds.ac.uk/staff/profile.php?tag=Gamper) and Dr Joanna Leng (http://www.joannaleng.com/) who have combined expertise in super-resolution microscopy, cell biology, image analysis, visualisation and ML. Please get in touch now

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