This opportunity is now closed.

Funded PhD Opportunity

CHIC: Behavioural modelling based on opportunistic sensing techniques within IoT (Computing)

This project is funded by: Invest NI Connected Health Innovation Center

Subject: Computer Science and Informatics


Summary

Connected Health Innovation Centre: In 2013 Invest NI committed to create a number of multi-million-pound business research centres, in key business areas, through the establishment of industry led competence centres. The Connected Health Innovation Centre (CHIC) is a competence centre that focuses on the Connected Health market. In 2019 Invest NI extended the support of CHIC through funding for a further 3 years. This funding supports a number of strategic research projects as well as Doctoral research in order to ensure research produced by CHIC advances the state of the art. Connected Health embraces a number of areas and focuses on the use of technology to improve health care. It will utilise technology to provide opportunities for care provision, diagnostics and support beyond the hospital setting.

This studentship will reside within the School of Computing, Faculty of Computing, Engineering and the Built Environment at Ulster University based at the Jordanstown campus. The project is fully aligned with the research strategy of CHIC. Project Summary: Understanding human behaviour in an automatic and non-intrusive manner, constitutes an important and emerging area of research within Connected Health. With the rapid development of the Internet of Things (IoT), combined with advances in machine/deep learning, technology-based solutions to automatically detect and model human behaviours are becoming possible. This technology can support services such as activity recognition, fall detection, behaviour modelling and risk determination. There is, however, still much research required in order to ensure these solutions are robust and that they can be adopted for use within real world environments. This PhD research will investigate the use of unobtrusive sensing technologies for modelling of human behaviours in real world settings. The project will address issues around personalisation and adaption of these models through the application of semi-supervised approaches to data annotation and federated learning to provided smarter, lower latency and less power intensive models that preserve user privacy.


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 65%
  • Research project completion within taught Masters degree or MRES
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Experience of presentation of research findings
  • A comprehensive and articulate personal statement

Funding

This project is funded by: Invest NI Connected Health Innovation Center

The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


Other information


The Doctoral College at Ulster University


Reviews

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

Watch Video  

Key dates

Submission deadline
Monday 2 December 2019

Interview Date
December


Applying

Apply Online  


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Professor Christopher Nugent


Other supervisors

Related Funded Opportunities

Computing

Subject: Computer Science and Informatics

 View details

Intelligent Systems

Subject: Computer Science and Informatics

 View details

Computer vision for advertising analytics

Closing date:
Wednesday 4 December 2019
Subject: Computer Science and Informatics

 View details