Artificial Intelligence and Machine Learning for Efficient Collaborative Design
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.
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.
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. The studentship covers UK/EU level fees. In addition to the basic tax free student stipend of £15,009 pa, R-R will provide a further tax free stipend increment of £9,000 pa. The stipend will rise in subsequent years. Funding for travel to international conferences will be available.
Click 'Visit Website' below and follow the link to apply online. Select the programme - PhD in Engineering and the Environment. Please enter the title of the PhD Studentship in the application form. As part of the selection process, the strength of the whole application will be taken into account, including academic qualifications, personal statement, CV and references.
For further guidance on applying, please contact [email protected]
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FTE Category A staff submitted: 192.23
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