PhD Studentship: Artificial Intelligence Approach to Holistic Design

Loughborough University

Preferred Start date (if any): January 2018

Supervisors:

Primary supervisor: Radmehr Monfared

Secondary supervisor: Darren Watts

Industrial Supervisor: Andrew Norwood  (MTC)

Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Project Detail:

Design traditionally follows an iterative process of applying a set of widely used tools and methodologies across all sectors. It is heavily reliant upon the designer(s) personal knowledge, experiences, and information available such as various design tools, sector of industry, manufacturing processes, quantities, standards, patents, legislation, suppliers etc.

However, the main drawbacks of the current design process are that it is often inherently slow, with the final design outcome being inconsistent and heavily influenced by the designers/engineers’ background and preferences. A designer consider all available design methodologies, hence many design(s) are different and typically far from optimal. In contrast, designs from the natural world that have evolved (iterated) over millions of years have repeatedly been shown to be both multidisciplinary and optimal.

This has led to the growing field of Biometrics, where mankind adopts nature’s design rules to similar problems. The biometric approach combined with the artificial Intelligence (A.I.) offers a huge opportunity for the design community to step towards optimised designs.

This research will investigate the feasibility of how A.I., through biometrics and algorithm-driven design can improve and optimise the design process in high value manufacturing industries.

The work will define how A.I. will learn and how it will interrogate and make use of available information in order to create truly optimised solutions in a cyber-physical environment. This project will build on existing scholarly research and would aim to attract additional industry support.  This research will primarily target industrial design problem commonly identified in high value manufacturing and design industries.

The PhD is jointly sponsored by Loughborough University and the Manufacturing Technology Centre (MTC). The applicant is therefore expected to spend significant of time at the MTC.

Funding information:

Please note that up to 4 studentships will be awarded on a competitive basis to applicants to this project and/or the following projects that have been prioritised for funding; job advert ref: NM180717, LZ190717, CL270717, PK200717, RM280717, TB281707 & SM280717.

If awarded, each 3 year studentship will provide a tax-free stipend of £14,553 p.a, plus tuition fees at the UK/EU rate (currently £4,195 p.a). While we welcome applications from non EU nationals, please be advised that it will only be possible to fund the tuition fees at the international rate and no stipend will be available. Successful candidates will be notified by 15/11/2017.

Find out more:

http://www.the-mtc.org/

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Engineering or relevant science subjects. A relevant Master’s degree and/or experience In programming languages will be an advantage. The candidate should have an interest in A.I. and ideally some knowledge or experience of engineering design, design optimisation or biometics.

Contact details:

Name: Radmehr Monfared

Email address: R.P.Monfared@lboro.ac.uk

Telephone number: 44-1509-227561

How to apply:

All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under Mechanical Engineering , Wolfson School  

Please quote reference number: RM280717

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Type / Role:

PhD

Location(s):

Midlands of England