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Artificial Intelligence and Machine Learning for Efficient Collaborative Design


Faculty of Engineering and Physical Sciences

This project is no longer listed on FindAPhD.com and may not be available.

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Dr D Toal No more applications being accepted Competition Funded PhD Project (European/UK Students Only)

About the Project

Supervisor: Dr. D.J.J. Toal
Co-supervisor Prof. A.J. Keane

Project description

Modern high value engineering design activities can involve collaboration between many engineers across many departments and perhaps even different countries around the world. In order to produce the best product possible such collaborations should be as seamless as possible thereby reducing risk and rework. The aim of this project is to explore ways in which artificial intelligence and machine learning techniques can aid such collaborations with a particular focus on the exploitation of multiple levels of simulation fidelity. The proposed frameworks will be applied to the design of an aircraft propulsion sub-system (gas turbine, nacelle and pylon) in collaboration with Rolls-Royce Plc.

In this project results from aerodynamic and structural analysis will be adapted to work with a range of data handling and modelling tools. This will permit the full range of engineering analysis methods to be tested in more collaborative settings. Combined with the latest GPU hardware, Deep Learning, Data Mining and Artificial Intelligence methods to support cross site working, the project will provide insights into the next generation of engineering design software.

This project is funded by Rolls-Royce plc as part of their support to the R-R University technology Centre for Computational Engineering at Southampton. In addition to the basic tax free student stipend of £15,141pa, R-R will provide a further tax free stipend increment of £9,000 pa and therefore in line with their graduate recruitment schemes. The stipend will rise in subsequent years. Funding for travel to international conferences will be available.

If you wish to discuss any details of the project informally, please contact Prof Andy Keane, Computational Engineering and Design Research Group, Email: [Email Address Removed], Tel: +44 (0) 2380 59 2944.

Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: full tuition plus, for UK students, an enhanced stipend of £15,009 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online here selecting “PhD ?? (Full time)” as the programme. Please enter ?? under the proposed supervisor.

Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact: [Email Address Removed]
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