2020 SINAPSE ASM Jun 19, 2020 09:00 AM - 05:00 PM — Virtual Meeting (online)
3rd International Conference on Medical Imaging with Deep Learning Jul 06, 2020 - Jul 08, 2020 — Virtual Meeting (online)
Medical Image Understanding and Analysis Conference 2020 Jul 15, 2020 - Jul 17, 2020 — Virtual Meeting (online)
CAFACHEM 2020 Summer School on Organic & Halogen Radiochemistry Aug 25, 2020 - Aug 28, 2020 — KCL Waterloo Campus, London
Scottish Dementia Research Consortium Annual Conference 2020 [rescheduled] Sep 07, 2020 10:00 AM - 04:00 PM — Radisson Blu, 301 Argyle St, Glasgow

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

Vacancy: Research Associate in Signal Processing and Machine Learning for Microendoscopy Imaging in Edinburgh

A Research Associate position is available at University of Edinburgh, to develop signal and image processing and machine learning algorithms for a breakthrough microendoscopy imaging system

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

For further information see https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=047911

Closing date: 10th June 2019