About the Project
Rolls-Royce develops advanced power generation and propulsion technologies for the aerospace, maritime and defence sectors. The complexity of these systems means that product design, certification and maintenance is driven by rigorous processes, accrued expertise and tacit knowledge. The level of human intuition in this process significantly increases the effort required to develop new products, certify them and minimise the downtime during operation. There is huge potential for AI to improve quality, reduce costs and deliver products more quickly and efficiently. Opportunities range from using machine learning to rapidly understand and add value to existing data, making engineers more efficient, to the use of automation in product design, engine testing and predictive maintenance.
In a safety critical environment such as aerospace, there are additional challenges to overcome to assure the regulators that innovations in AI are safe to use. Existing machine learning systems provide good “average case” performance in specific domains; for example, online film recommendations or predicting the demand for product sales. While this is very helpful for consumer-facing web based AI, there is a big gap between these approaches and the industrial and engineering applications of AI where systems cannot fail.
In this PhD, you will work on advancing the state of the art in machine learning to meet the challenge of mitigating “worst case” performance in systems that must be reliable and trusted not to fail, for example in new efficient engines or control systems for autonomous vehicles. You will develop new AI that will work with and for some of the world’s best engineering teams to design better products and systems that improve performance and efficiency while also improving safety and reducing the likelihood of costly or catastrophic failure. Key questions that could be addressed by your research include:
· How do we design new machine learning systems to work alongside expert engineers?
· How can we guarantee that machine learning recommendations will be safe and reliable?
· How can machine learning work transparently to turn data into new knowledge and insights for current and future problems?
· How can we build trust in the outputs of machine learning systems?
· How can we demonstrate to regulators that critical systems designed using AI are safe to use?
This PhD project provides a unique training opportunity with a chance to implement your ideas across a wide range of exciting applications, such as new engines and autonomous vehicles, to improve quality, effectiveness, traceability and accountability in production, service and maintenance. You will have the opportunity to work directly with data scientists in Rolls-Royce’s R² Data Labs and obtain world-leading industry experience of how innovations in machine learning and related technologies will be used in the future.
Through the unique interdisciplinary approach of the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, you can demonstrate not only the transformative potential of AI within this field, but also how we will meet the wider challenges and prove to the regulatory authorities that AI technology is both effective and safe to use. Consideration will also be given to policy and procedural advice on how to govern this emerging technology, understanding the cultural and societal impact upon both the engineering community and regulatory authorities while also considering where the technology may need to be adapted to build trust and confidence with the regulators.
Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree or equivalent.
Informal enquiries about the project should be directed to Dr Neill Campbell.
Further details of the Centre for Doctoral Training can be found at: http://www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/. Enquiries about the application process should be sent to [Email Address Removed].
Formal applications should include a research proposal and be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002
This ART-AI CDT studentship is available on a competition basis for UK students for up to 4 years. Funding will cover Home tuition fees, maintenance at the UKRI minimum doctoral stipend rate (£15,285 per annum in 2020/21, increased annually in line with the GDP deflator), and a training support fee of £1,000 per annum.
We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.