This funding model includes a 36 month fully funded PhD Studentship, set in-line with the Research Council values. For 2024/5, this will be £19,237 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Monday 30th September 2024 for a start date of the 3rd February 2025.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
If your question is not answered above and you need any further information, please use the contact details below:
- For enquiries about the project content, please contact: chris.roberts@bcu.ac.uk
- For enquiries about the application procedure, please contact: research.admissions@bcu.ac.uk
Project Title:
Applications of AI in highways safety science research: development of a decision support tool
Project Lead:
Dr Chris Roberts
Project ID:
CEBE- 42115232
Project Description:
Artificial intelligence (AI) applications in data science and management are myriad but the UK government company, National Highways (managers and members of their supply chain) have had varied experiences with this disruptive digital innovation. Big data sets are frequently transformed into ‘information’ but subsequent steps to acquire ‘knowledge’ and ‘wisdom’ from information presented remain elusive due to scant interpretation capabilities within, and the sheer voluminous size of information presented to, industry practitioners. These issues often result in the inherent capabilities of AI not being used to their optimum potential to resolve the most significant problems (most notably ‘safety science’ related areas) within the business. Worst still, incorrect applications of AI are adopted with disastrous results not least for the reputation of the technology.
Anticipated findings and contributions to knowledge:
This present research proposes to develop a decision support tool to generate a reflexive process solution that allows users to critique, appraise and evaluate how subjective applications of AI in various ‘safety science’ contexts produce optimum (or otherwise) results.
Grounded theory will be adopted within a participant action research context to gather primary data (via ‘exploratory mixed methods’) through observational research, questionnaires and interviews with National Highways (and members of their supply chain) staff. Machine learning algorithms and/or multivariate statistical analysis will be used to validate a process modelling tool that will drive organisational learning to inform future policy guidance on the optimum applications of AI.
Person Specification:
Successful applicants will have graduated (or be due to graduate) with an undergraduate first-class degree and/or MSc distinction in a relevant field of science. Preferably, applicants must also demonstrate good knowledge of applicable digital technologies.
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.