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  Developing smartphone-based tools for capturing and visualising outcome measures in long-term health conditions


   Department of Computer Science

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  Dr Simon Jones  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

Supervisory Team:
1. Simon Jones (Dept of Computer Science, University of Bath)
2. Dario Cazzola (Dept for Health, University of Bath)
3. Darren Cosker (Dept of Computer Science, University of Bath)
4. Raj Sengupta (Royal United Hospital / Royal National Hospital for Rheumatic Disease, Bath)
5. Matthew Young (Dept of Computer Science, University of Bath)

We are looking for a motivated candidate, with a background in areas such as computer vision, machine learning, data visualisation and human-computer interaction to work on an exciting new collaborative project with the Royal National Hospital for Rheumatic Diseases (RNHRD).

The project will focus on designing, developing and evaluating tools to support smartphone-based methods for capturing and visualising outcome measures for Ankylosing Spondylitis (AS), a long-term inflammatory condition.

The RNHRD has played a leading role in developing the scales used worldwide to monitor AS disease progression, collectively known as the Bath Indices. These include monitoring the changes in spinal movement capability.

It is envisaged that a complete smartphone-based tool can be developed allowing for objective, home-based, capture of the Bath Indices to create an accessible application to support long-term care.

A successful assessment tool would have significant impact for both future academic research and clinical trial/assessment. This will reduce the risk of clinician error and provide a method for patients to self-monitor their condition between appointments. New visualisation tools, presenting information generated using the automated measurement methods will provide valuable feedback for both patients and clinicians regarding disease progression and generate new knowledge about the impact and management of flares.

Application:

The successful candidate should have (or expect to have) a UK Honours Degree (or equivalent) at 2.1 or above in Computer Science or similar. Other related backgrounds can also be considered, like Engineering or Mathematics with computer algorithms experience. Desirable experience would include some knowledge of either machine learning or computer vision for practical purposes.

Informal enquiries should be directed to Dr Simon Jones, [Email Address Removed] or Mr Matthew Young, [Email Address Removed].

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science:
https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/




Funding Notes

The successful candidate will receive a studentship funded by the University of Bath and the Royal United Hospitals Bath NHS Foundation Trust. The studentship will cover Home/EU tuition fees and a stipend (£14,777 in 2018-19) for a period of 3 years as well as providing a budget for research expenses.

NOTE: ONLY UK and EU applicants are eligible for this studentship. We are not able to consider applicants who are classed as Overseas for fee paying purposes.

References

(1) Zochling, J. Measures of Symptoms and Disease Status in Ankylosing Spondylitis. Arthritis Care and Research. 2011;63(S11):S47-S58)

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