PhD Studentship: Developing Artificial Intelligence Tools to Support Occupational Health Monitoring of Whole Body Vibration Exposure

PhD Supervisor: Thomas Blumensath

Supervisory Team: Ying Ye (ISVR), Thomas Blumensath (ISVR)

Project description

Whole body vibration (WBV) is vibration transmitted through a seat or the feet and is often coupled with shock. It is generally accepted that excessive WBV exposure leads to injury, especially if linked to poor posture, but causation and dose-effect relationships have not been clearly established. The injuries are also less clearly defined and are often non-specific, for example, neck pain, back pain or dizziness, all common symptoms that are difficult to attribute directly to levels of WBV exposure.

To address these challenges, we are here interested in the application of advance machine learning tools and in particular natural language processing methods. The use of these approaches is now revolutionising many fields of science, where their ability to automatically evaluate, summarise and analyse large text data-sets has lead to many breakthrough discoveries recently.

Working at the world renowned Human Factors Research Unit at the Institute of Sound and Vibration Research, you will join a well-established group of PhDs and Doctoral researchers specialising in human response to Sound and Vibration. The Human Factors Research Unit (HFRU) within the ISVR is recognised as a world-class leader in human responses to vibration, with extensive international and multi-disciplinary collaboration. We have unique facilities including motion simulators, instrumentation and software, and a dedicated human vibration library. Our research has undertaken measurement, evaluation, and assessment of motions in a wide range of transport on land, sea and in the air, including measurements on a wide range of vehicles, helicopters, and ships and our research forms the basis of many international standards.

You are invited to join us to pursuit a PhD in the development and application of novel Machine Learning tools to automatically analyse text records in order to discover new risk factors and dose effect relationships to support occupational health monitoring, looking particularly into the link between WBV exposure and muscuskeletal disorders. According to the Health and Safety Executive, muscuskeletal disorders, such as back-pain, account for 27% of new and long-standing cases of work-related ill health in 2021/22. Understanding the risk posed by WBV exposure and the complex interrelationship with other risk factors is thus key to the avoidance of chronic conditions and acute injuries.

As this project might involve the handling of MOD health records, successful candidates might be required to undergo successful security screening.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent, with internal studentship funding likely to require a first class honours degree or equivalent).

Closing date: 31 March 2024

Funding:

For UK students, Tuition Fees and a stipend of £18,198 tax-free per annum for up to 3.5 years.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Thomas Blumensath

Applications should include:

• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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