About the Project
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
The PhD will be based in the School of Computing, Mathematics and Physics and will be supervised by Dr Rinat Khusainov and Dr Richard Curry.
Project Highlights:
The work on this project will include:
- Applying the latest Artificial Intelligence techniques
- Developing technologies that can help improve people's health and wellbeing
- Experimenting with realistic application scenarios and interacting with potential users of your research
Project description:
Applications of AI in health and wellbeing technologies offer unprecedented opportunities for enhancing our health and wellbeing, for preventive and personalised care, and for promoting independence. Wearable devices, smart environmental sensors, and user feedback can generate valuable data that can be processed by AI algorithms to contribute to early disease detection, management of long term conditions, and assisting with everyday activities.
This PhD is about investigating how a range of AI techniques, including machine learning, computer vision, natural language processing and Large Language Models, can be used to develop next generation health technologies focusing on wellbeing outside clinical settings. The aims are to help people stay healthy at home and in a workplace, to facilitate early hospital discharge, and to promote healthy aging. Examples include ambient assisted living, health apps, and employee wellbeing platforms.
We are looking for motivated numerate candidates, wishing to combine their interest in AI with a passion for innovation to research novel practical applications of AI in health and wellbeing technologies. The successful candidate will work within a team of academics and researchers with an established track record in applied AI for health and wellbeing, and strong links with care organisations, technology providers, and end user groups. We have a friendly and supportive research environment and excellent research facilities, including a fully instrumented residential house providing a real-world environment for experimentation with various technologies, an IBM PowerAI Vision platform, and the Sciama supercomputer for demanding machine learning tasks.
General admissions criteria
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master's degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications.
English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office.
Specific candidate requirements
You'll have good numeracy and programming skills. Knowledge of machine learning, computer vision, natural language processing / LLMs, as well as experience with sensors are helpful.
How to Apply
We'd encourage you to contact Rinat Khusainov (rinat.khusainov@port.ac.uk) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our 'How to Apply' page offers further guidance on the PhD application process.
When applying please quote project code CMP10670529.
Funding Notes
Funding Availability: Self-funded PhD students only
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only - eligibility criteria apply).
Bench fees
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.