Chronic musculoskeletal pain is a prevalent condition where pain persists in the absence of injury or tissue damage and is associated with dysfunction in the nervous system. People with chronic musculoskeletal pain report finding harmless, valued everyday physical activities challenging. Findings from previous research suggest that automatic detection of pain and related states could be valuable for providing tailored feedback or support that facilitates engagement in these activities. This project will advance research in the area by investigating detection of more fine-grained descriptions of pain (and related) experience than have previously been addressed.  

The project sits within applied machine learning research exploring, developing, and evaluating automatic detection technologies aimed at supporting self-managed rehabilitation and with a user-centred approach that draws from understanding of the relevant needs of people with the condition. It will involve machine learning research, evaluation of machine learning models in the real world, creation/extension of datasets, and review/analysis of relevant literature.  

The candidate will benefit from mentoring and opportunities for professional development via the supervisor and the university. They will be supported in dissemination of their work in relevant venues and active involvement in relevant research communities.   

The stipend is available to: Home, EU and Overseas. 

The fee waiver is available to: Home, EU and Overseas. 

Eligible candidates will have an upper second-class (2:1) undergraduate degree or above, in a relevant field.  

Candidates will also have strong experience in machine learning; programming skills; curiosity & analytical thinking; experience working independently on a research project (e.g. final year undergraduate project); interest in the proposed research 

Apply via the postgraduate application system for a full time PhD in Informatics (JAN25).  

Please upload a copy of your degree transcripts and certificates, English-language test (if required), two academic references, research proposal, CV and a statement of purpose. For guidance on the application process, please see here.   

Please clearly state on your application that you are applying for the Applied machine learning for chronic pain physical rehabilitation scholarship under the supervision of Dr. Temitayo Olugbade.