UKRI Innovation/Rutherford Fund Research Fellow

Swansea University - Swansea University Medical School

Salary: £33,518 to £38,833 per annum (pro rata if part time), together with USS pension benefits if required.

The normal expectation is that the successful applicant will be appointed to the minimum of the agreed scale with annual increments on 1 October each year (subject to completing six months service).

Please note – Due to restrictions around the post funding, the successful applicant MUST be available to start in the role on 15th February 2018 at the latest. Shortlisted candidates will be contacted in the first week of January 2018 and will be invited to interview in the second week of January 2018

How to apply:

  • Candidates will be shortlisted on the basis of their suitability against the listed essential criteria.
  • Candidates are also required to provide a CV and a research/project summary that aligns with the MRC research priorities outlined below. Templates for both of these are available and should be completed and attached with the application.

Main Purpose of the Post
This post is funded as part of the Medical Research Council’s commitment to support post-doctoral fellowships for Health Data Research UK. As part of a £12m package of support to build a critical mass of interdisciplinary skills, the MRC is funding two new early and mid-career post-doctoral health data science Fellowships to be based at Swansea University. Awards to non-UK individuals will be called Rutherford Fund Fellowships. Awards to UK nationals will be known as UKRI Innovation Fellowships. The Fellowships will provide three years of personal support (salary and research costs), covering both clinical and non-clinical trainees and including opportunities for technology specialists outside of the traditional academic PI track.

The Fellowships will link to the UK’s Industrial Strategy through the development and application of novel analytical approaches that could be commercialised by pharma and technology industries. Opportunities exist both within and beyond the Medical School (MS) at Swansea University. Examples of potential projects include: developing and applying multivariate generalised linear mixed models (Berridge, MS); developing novel machine learning methods for the detection of dementia and high-order epistasis, and for unravelling patterns of polypharmacy from electronic health records (Zhou, MS); predicting a diagnosis of spondyloarthritis using a data driven analysis approach (Brophy, MS); Systems Oncology Toolbox: multi-scale optimisation of cancer treatments with the help of patient-specific data (Powathil, Maths); exploring the effects of genetic variation through linkage of whole exome sequencing and routinely-collected healthcare records (Rees, MS); enriching routinely-collected healthcare records with natural language processing (Ford, Lyons, MS); non-invasive, fractional flow reserve (FFR) calculations on mobile devices; non-image based aneurysm detection (Nithiarasu, Computational Engineering). Applications from individuals specialising in AI, machine learning and natural language processing as applied to structured and unstructured health data are particularly welcome.

Informal enquiries are welcome and should be directed to Professor Damon Berridge, on +44 (0)1792 606191or on d.m.berridge@swansea.ac.uk  

This post will close at 11.00pm 31st December 2017.

Applicants will find full job details together with the online application link http://www.swansea.ac.uk/personnel/jobs/details.php?nPostingId=5704&nPostingTargetId=9532&id=QHUFK026203F3VBQB7VLO8NXD&LG=UK&mask=suext

The University is committed to supporting and promoting equality and diversity in all of its practices and activities. We aim to establish an inclusive environment and particularly welcome applications from diverse backgrounds.

Swansea University is a registered charity. No. 1138342.

Share this job
     
  Share by Email   Print this job   More sharing options
We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Location(s):

Wales