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UPDATE: We are delighted to announce that EPSRC have funded our CDT for five more years under the new name EPSRC Centre for Doctoral Training in Intelligent Integrated Imaging in Healthcare (i4health).

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17 Jul

Identifying early and progressive phenotypes of fibrosing lung disease

Allocated Projects

A PhD studentship (4 year funding – MRes + Phd, with a tax free stipend of £17,777) sponsored by GlaxoSmithKline is available in the UCL Centre for Medical Image Computing The successful candidate will join the UCL CDT in Medical Imaging cohort and benefit from the activities and events organised by the centre.

Lung cancer screening studies image the lungs using low-dose volumetric CT techniques to identify cancer in high-risk patient groups. However, the detailed focus on lung cancer detection has meant that recognition of other diseases on lung-cancer screening CT images has been neglected.

One such under-recognized disease is lung fibrosis, which predominantly affects smokers and accounted for 0.9% of all UK deaths in 2012. Lung fibrosis is typically diagnosed at an advanced stage once patients are already compromised as CT appearances and lung function tests are only well characterized for established, relatively late-stage disease.

UCL is to start one of the largest lung cancer screening studies ever performed in the world, called SUMMIT, which will scan 25,000 patients annually for 3 years with low-dose CT imaging. Our study, which is a collaboration with GlaxoSmithKline, will use computer-based supervised and unsupervised machine learning techniques to characterize CT patterns indicative of early lung fibrosis in the screening population. Through linkages to patient genomic data, we aim to identify patient groups that have a propensity to develop lung fibrosis and a patient subset that manifest rapid disease progression. Our overall aim is to reimagine what we know about the early stages of lung disease, particularly lung fibrosis. In so doing, we aim to improve subtyping of lung disease phenotypes, and for lung fibrosis, identify poor prognostic patterns independent of a patients underlying diagnosis.

The candidate is expected to have at least an upper second-class degree in physics, engineering or related area and a Masters degree or equivalent in a relevant subject area. A strong mathematics background is essential. Good working knowledge of C++ and/or Python or MATLAB is preferable. The candidate must be committed to deliver excellence in research, and will also be expected to provide regular, biannual reports on research progress and present at international conferences.

As the post is funded via the EPSRC, applications are restricted to candidates from the UK or EU, though EU candidates must have been living and/or working in the UK for 3 years prior to the application date.

To make an application please send a CV and contact details, including email addresses for two referees, to Dr Joseph Jacob at Please include a covering letter indicating why you believe you are suitable for the studentship, your long-term research and professional goals, and any particular expertise you have that you feel may be applicable in this work.

Closing date: 30th of July 2018, with an anticipated start date of October 2018.