The Advanced Manufacturing sector will be profoundly changed as generative AI (GAI) develops – many consider it as the ‘next generation industrial revolution’. If the potential of this evolving technology and its promise in increasing productivity is to be fully realised, the engineering community and its educators must develop a better understanding of the skills that technicians and graduate engineers will need to create the so-called gear shift that the sector needs. A classical process and systems engineering approach will be needed to adapt and embed digitalisation within manufacturing organisations.
GAI will also have a transformational impact in education but engineering educators do not yet understand how its disruptive capability can be exploited to upskill engineering students with the digital know-how and practical understanding of process engineering such that productivity improvements in industry will be intuitive and attainable. Safeguards around predictive and ethical behaviours, safety and robustness, as well as societal benefits are not yet understood within an engineering context.
The research project is comprised of an extensive literature review, product design specification, training model development and test, publication of findings and thesis submission.
This research programme seeks to:
1. identify the skills and aptitudes that will be needed by the engineers of the future if they are to be competent designers and users of AI in manufacturing organisations;
2. use AI to develop industrially relevant training packages for students that could be embedded within the wider UG engineering curriculum;
3. extend the training packages developed in (2) to encompass novel technologies more broadly;
4. create a centre of excellence within AMIC (and AFM2) that focuses on the skills requirements for successful transition between TRL 3 and 4 such that novel technologies can be competently translated to manufacturing competences (building on previous work that was carried out with Spirit Aero, formally Bombardier).
This research project will appeal to engineering and/or technology graduates with an interest in manufacturing processes and programming.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
The University offers the following levels of support:
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies. Further information on cost of living
Chan, C.K.Y. and Hu, W. (2023) ‘Students’ Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education’, International Journal of Educational Technology in Higher Education, 20. doi:10.1186/s41239-023-00411-8.
Johri, A., Katz, A.S., Qadir, J. and Hingle, A. (2023), Generative artificial intelligence and engineering education. Journal of Engineering Education, 112: 572-577. https://doi.org/10.1002/jee.20537
Beagon, U., & Bowe, B. (2023). Understanding professional skills in engineering education: A phenomenographic study of faculty conceptions. Journal of Engineering Education, 112(4), 1109–1144. https://doi.org/10.1002/jee.20556
K. Boettcher, C. Terkowsky, M. Schade, D. Brandner, S. Grünendahl & B. Pasaliu (2023) Developing a real-world scenario to foster learning and working 4.0 – on using a digital twin of a jet pump experiment in process engineering laboratory education, European Journal of Engineering Education, 48:5, 949-971, DOI: 10.1080/03043797.2023.2182184x
Hadi, Muhammad Usman; tashi, qasem al; Qureshi, Rizwan; Shah, Abbas; muneer, amgad; Irfan, Muhammad; et al. (2023). Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects. TechRxiv. https://dx.doi.org/10.36227/techrxiv.23589741.v3
Submission deadline
Monday 26 February 2024
04:00PM
Interview Date
March 2024
Preferred student start date
16th September 2024
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