Real-time microendoscopy has been dominated and limited to intensity mode imaging due to existing detector technology. This limitation has been overcome using new sensors that incorporate intensity and lifetime imaging, developed as part of the EPSRC-funded Proteus project. This technology enables multidimensional high content high-resolution real-time sensing and imaging of dynamic biological processes and is poised for disruptive healthcare impact.
The research associate will aid future deployment of these systems into clinical environments by addressing issues of transmitting high-volumes of data from the sensor head, improving image modelling across fibre-cores and developing algorithms for in-situ quantification of imaging targets such as cellular activation, enzyme kinetics, and drug-target engagement. The role involves contributing signal and image processing and machine learning expertise to the project by developing, to near-clinical readiness, novel state of the art signal processing and machine learning algorithms to improve the quality of the data received from a sensing system called Kronoscan. There will be a strong emphasis on developing robust real-time algorithms.
The successful candidate will be a member of the Institute for Digital Communications in the School of Engineering and be jointly based at the Queens Medical Research Institute (QMRI) at the University of Edinburgh. A significant proportion of time will be spent in the QMRI developmental interventional technologies (DIT) facility with daily interaction with clinicians, biologists, chemists, physicists and engineers.
The post is offered on a full time fixed term period for 36 months.
Informal enquiries are encouraged to James.Hopgood@ed.ac.uk
Closing date: 10th June 2019