Intelligent Data Processing to Support Self-Management and Responsive Care Studentship

An opportunity to apply for a funded full-time PhD in the Faculty of Environment and Technology, UWE Bristol. The studentship will be funded by UWE Bristol.

The expected start date of this studentship is 1 May 2017.

The closing date for applications has been extended to Friday 3 March. 

About the Studentship

Recent advances in non-intrusive activity and physiological data sensing sensors and data analytics have enabled pioneering work in the area of ambient assisted living. Decisions based on data from sensors can now be made in near real-time and can facilitate feedback to support self-management of activities of daily living.

This PhD will be an evidence-based research study that uses a range of real sensor data relating to older adults with ageing-related impairments, fusing data from multiple sensors to provide a holistic real-time view to support their daily living and care needs. This research builds on existing research into ambient intelligent systems, as well as internet of things technology and big data analytics.

The aim will be to present actionable information to both people and carers in an intuitive manner that enables effective cognitive processing and proactive decision making to facilitate self-management and independent living.  The work will therefore also involve co-design activities with care home residents, older adults with ageing-related impairments, and their specialist carers to determine specific long-term independent-living care needs. The information will help with structuring daily activities and tasks, as well as supporting well-being by utilising sensor data to identify patterns of activity and provide prompts to support healthy-living behaviours.

This research is fundamental to designing and developing appropriate responsive and persuasive interventions, which will serve as cognitive assistants or agents and will link to a complementary PhD at Coventry University.

The PhD programme will benefit from support from the Bristol Robotics Laboratory’s Assisted Living Studio and research on behavioural activity analysis and recognition and UWE’s work in Data Fusion, Integration and Mining from its Centre for Complex Cooperative Systems together with external supervisory support from Coventry University’s School for Computing, Electronics and Maths and Centre for Technology Enabled Health Research.

For an informal discussion about the studentship, please email Associate Professor Praminda Caleb-Solly:  

Funding details

The studentship is available from 1 May 2017 for a period of three years, subject to satisfactory progress and includes a tax-exempt stipend, which is currently £14,296 per annum. 

In addition, full-time tuition fees will be covered for up to three years (Home/EU rates only). Overseas applicants will be required to cover the difference between Home/EU and the overseas tuition fee rates in each year of study.

Eligibility criteria

Applicants must have a good honours degree in the field of engineering sciences, computer science or any related field with a specialisation in signal processing, sensors and statistical data analysis. A Master’s degree is preferred. They should have evidence of excellent mathematical and programming skills in C/C++ and MATLAB or similar. Experience in machine learning is desirable.

A recognised English language qualification is required.

How to apply


Please submit your completed application to by Friday 3 March.

If it is not possible to submit your application via email, you may post your application to the following address:

UWE Bristol Graduate School
Room 3E37
University of the West of England
Coldharbour Lane
Frenchay, Bristol
BS16 1QY

Please be aware your application needs to arrive by the deadline date so please allow extra days for posting.

Application forms

Please complete and return the following forms:

Each of your referees must complete and return the following reference form to you as part of your application:

Please consult the application Guidance notes when completing the above forms.

Please note: When submitting 'supporting documents' electronically, please ensure that all scanned documents are saved in black and white so that the size of the document is kept to a minimum.

Further information

Interviews will take place between Monday 13 March and Monday 20 March. If you have not heard from us by that date, we thank you for your application, but on this occasion you have not been successful.

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