REFERENCE NUMBER
Please select SCEBE/23STU/Enemali
BACKGROUND AND AIMS
Artificial intelligence (AI) has great potentials that can be harnessed for innovative healthcare applications. It can enable personalised healthcare solutions, with the advantage of fast, convenient and efficient monitoring. However, to harness greater potentials of AI in healthcare, Edge (embedded) devices are crucial. Not only do they enable real-time healthcare solutions and improve user convenience, but they also offer several other advantages, e.g. energy and cost efficiency.
The advent of Internet and pervasive nature of modern computing has led to the rapid developmentof the Internet of Things (IoT) devices. Healthcare industry has also seen rise in use of many IoT devices for remote patient monitoring. Multiple wearable devices can now be synced with smarty watches and smartphones and their use is on the rise. These sensor-enabled devices can perform a range of operations from benign ones such as monitoring blood pressure, glucose level, sleep using devices such as CPAP, etc., to sending critical data such as on heart conditions from IoT based pacemakers. Such a situation presents a challenging landscape both in terms of the device security and data privacy.
This PhD project is focussed on developing smart edge based health monitoring solution for a major SDG in Good Health and Well being challenge in Ensure healthy lives and promote well-being for all at all ages.
The successful candidate is advised to look at current projects: (https://www.gcu.ac.uk/cebe/research/smarttechnologycentre/projects/) to identify which areas are of interest to the SMART Technology Centre and develop a proposal.
Candidates must include an outline of their ideas for exploring big data / machine learning approaches for health applications for solving the societal challenge in health , drawing on relevant literature (via the ‘research proposal’ section of the application form; maximum of 750 words excluding references.
The successful candidate will have an Engineering (Power, Electronics, Telecommunications, etc.)/ Computer Science or Computing) and/or data science background (First Class or 2:1 Honours) and a Master’s degree (ideally at least Merit) in a related area or at the interface between the two (e.g. AI, machine learning or Engineering, Computer Science, Health sciences). They will have experience of neural network techniques or willing to learn it as well as experience and knowledge of some quantitative research methods. Prior work in health applications, is desirable.
HOW TO APPLY
This project is available as a 3 years full-time PhD study programme with expected start date of 1 October 2023.
Candidates are encouraged to contact the research supervisors for the project before applying.
Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using the Application Process page, choosing a October 2023 Start.
Applicants shortlisted for the PhD project will be contacted for an interview within six weeks from the closing date.
Please send general enquires regarding your application to: [Email Address Removed]
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