Closing date: 29 April 2024
Funding providers: Engineering and Physical Sciences Research Council (EPSRC) and RCNDE
Subject areas: Human-Computer Interaction, Machine Learning
Project start date:
Project supervisors:
Aligned programme of study: PhD in Computer Science
Mode of study: Full-time
Project description:
Many industries are relocating workers from hazardous environments to supervisory rooms, where they need to engage with automated systems to perform their work. A critical challenge in human-automated systems interaction lies in effectively managing human factors such as cognitive load (CL) and emotions such as stress to ensure optimal task execution. However, currently, measuring CL involves bulky, expensive techniques such as functional magnetic resonance imaging and magnetoencephalography, limiting their practicality. Recent progress in wearable design helps non-invasively measure CL and stress by synchronously analysing physiological indicators such as Pupillometry, heart-rate variability, and skin conductivity. Regardless, previous work involved wearing eye trackers and heart monitors, which may not be suitable for real-time inspection setups. Besides, the tasks utilised to collect data for estimating CL were superficial and consequently, CL measures were task dependent.
Thus, there is a clear need to develop less invasive and robust task-independent measures for CL. This need is particularly relevant in non-destructive evaluation (NDE), where automation is increasing, and human factors significantly contribute to uncertainties in inspection results. This project will explore corrective methods to enhance inspection reliability by developing robust measure of CL and correlating it with operator performance. With automated systems generating vast volumes of inspection data and data analysis tasks becoming more complex, addressing human factors becomes increasingly vital for ensuring the overall dependability of inspections.
For more details please see here: https://www.swansea.ac.uk/postgraduate/scholarships/research/computer-science-epsrc-rcnde-phd-real-2024-rs584.php
Candidates must hold an Upper Second Class (2.1) honours degree or an appropriate master’s degree with a minimum overall grade at ‘Merit’ in Computer Science, Mathematics or a closely related discipline. If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements (see country specific qualifications). Please note that you may need to provide evidence of your English Language proficiency.
Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations.
If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in.
This scholarship covers the full cost of tuition fees and an annual stipend at £19,237.
Additional research expenses will also be available.
To apply, please complete your application online with the following information:
In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.
*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.
One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.
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Informal enquiries are welcome, please contact Dr Muneeb Imtiaz Ahmad (m.i.ahmad@swansea.ac.uk).
*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.
Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations.
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