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Artificial Intelligence Methods - Automated Defect Recognition of Ultrasonic Data in NDT


Project Description

Applications are invited for a research studentship in the field of Machine Learning / Artificial Intelligence (AI) in non-destructive evaluation (NDE) techniques leading to the award of an Engineering Doctorate (EngD) degree.

The use of Machine Learning / Artificial Intelligence (AI) is now commonplace among a wide variety of industry sectors, including computer gaming, marketing and security. While some work has been carried out for NDT, there are a lack of commercially available systems that exploit the technology. Primarily this is due to the complex nature of NDT components, inspection parameters and obtainable data. Consequently, there is a real need to determine the applicability of a range of AI techniques for use in ultrasonic inspection and for NDT in a wider context.

Specifically, it should be investigated which AI approach is most applicable for a range of ultrasonic inspection methods. The proposed research will (through academic rigor) seek to establish a solid understanding of available AI methods establish their suitability for NDT. Flexible AI algorithms should then be developed for use with ultrasonically acquired data. The benefits and limitations of the chosen approaches should be assessed through a parametric study on real-world industrial samples. Although AI methods can possibly be used with a wide variety of NDT techniques, the scope of this project is to consider ultrasonic inspection for the well-defined girth-weld inspection scenario only. This is to ensure parameter inputs are minimised while techniques and algorithms are developed.

During the 4 years you will spend approximately 9 months on advanced technical and professional development courses – usually spending the first year at Bristol University and the remaining time at TWI Technology Centre, Wales. The project will require occasional travel on a short-term basis within the UK and overseas.


Further Particulars
The studentship is offered through the EPSRC Centre for Doctoral Training in Future Innovation in NDE (FIND CDT) which is a partnership between a select group of universities and companies offering a 4-year Engineering doctorate designed to launch outstanding graduates into an engineering career. With close links to the related UK Research Centre in NDE, students are part of a vibrant community of more than 200 researchers and have access to a range of technical training courses delivered by world leading experts.

The post is supported by a bursary and fees (at the UK/EU student rate) provided by EPSRC, together with a generous top up by the sponsor company, TWI Ltd.

Candidate Requirements
Applicants must hold a minimum of an upper 2nd class honours degree in Mechanical Engineering, Physics or a related subject.

Basic skills and knowledge required.
An enquiring and rigorous approach to research together with a strong intellect and disciplined work habits. Good team-working, observational and communication skills are essential.

Informal enquiries
For informal enquiries, please email Prof Paul Wilcox, or

For general enquiries, please email

Application Details
Prior to application Interested applicants should send an up-to-date CV to .

To apply for this studentship submit a PhD application using our online application system [www.bristol.ac.uk/pg-howtoapply]
Please ensure that in the Funding section you tick “I would like to be considered for a funding award from the Mechanical Engineering Department” and specify the title of the scholarship in the “other” box below with the name of the supervisor

Funding Notes

Scholarship covers full UK/EU (EU applicants who have been resident in the UK for 3 years prior to application) PhD tuition fees and a tax-free stipend of a minimum £20,000 subject to contracts and eligibility criteria.

Candidates can check the eligibility criteria for the award at View Website

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