PhD Studentships in Virtual Reality and Machine Learning

University of Wales - Wales Centre for Advanced Batch Manufacture (CBM)

Funding amount: 100% of UK/EU tuition fees paid and annual living expenses of £14,439 (2017/18 subject to 1% increase p.a.), plus a £5,000 Research Development Grant.

Duration: The funding covers a three-year PhD.

Wales Centre for Advanced Batch Manufacture (CBM)

CBM is an exciting new development that has been jointly established by the University of Wales and University of Wales Trinity Saint David. CBM is a manufacturing and research facility centred on the creative application of advanced technologies and techniques across a wide range of manufacturing sectors.  CBM’s research seeks to engage in ground breaking and rigorous academic research, utilising our in-house technology platform. Our research is aimed at providing practical and innovative solutions to industrial problems.

PhD researchers are integral to CBM’s research activities. Our researchers have full access to CBM’s staff and expertise, product development tools and additive manufacturing technology to support their research. Our PhD students thus play a key role in developing the research profile of the centre and enhancing our capabilities. 

For more information on CBM see:  

Fully-Funded PhD Studentships

CBM is offering fully-funded PhD studentships to undertake applied research in the following areas:

Virtual Reality to Enhance New Product Development

CBM aims to expand its research activity in the exciting new area of virtual reality. Virtual reality tools and techniques have developed to a point whereby they represent a cost-effective methodology to enhance the design, manufacture, and deployment of wide range of products. A potential application of virtual reality is in the area of specialist medical devices. CBM employs additive manufacturing technology for the fabrication of bespoke medical implants (e.g. titanium plates for complex maxillofacial surgery). The PhD research project could investigate how virtual reality technologies can accelerate the design of patient-specific devices through early-stage computer-aided surgery simulations. It is hoped that virtual reality will enable implants (and the associated cutting/drilling/repositioning guides) to be rapidly configured from the perspective of both the patient and the specific surgical team. Virtual reality technologies in this context have the potential to reshape the patient experience, improve clinical outcomes, deliver innovative new therapies, and better train healthcare professionals.

Machine Learning to Optimise 3D-Scanning Procedures

Numerous research projects have employed artificial intelligence and machine learning techniques for knowledge capture from 2D images (e.g. facial recognition from photographs). This PhD project will expand on this work and investigate the use of machine learning algorithms to automate the 3D scanning of anatomical features. CBM has a range of reverse engineering and 3D scanning technology in house. This technology is frequently employed to capture human anatomy to design a bespoke medical device (such as orthoses for wrists or ankles). The 3D scanning process generates a dense point cloud that needs to be converted into an accurate, usable 3D model. The aim of this research project is to evaluate how machine learning techniques can optimise key aspects of the 3D scanning process. It is anticipated that Python scripting for Rhino3D will be employed to automate the initial data acquisition through to optimised techniques for the manipulation and reconfiguration of complex anatomical 3D models.

For further details on each research project see: 

As part of the above research projects, candidates will have opportunities to undertake research and advanced training at a number of partner institutions, organisations, and companies, in the UK, Europe and beyond.

Candidate requirements: At least a 2:1 Honours degree in engineering, physics, computer science or a relevant discipline. A further qualification, such as a MSc, is advantageous. Knowledge of relevant programming languages (C++, Python, R, etc.) is also desirable.

Candidates must demonstrate an interest in the commercial application of research.

Funding: Home/EU: Full payment of tuition fees and maintenance stipend of £14,439 per annum. Studentship covers full UK/EU (EU applicants who have been resident in the UK for 3 years prior to application) PhD tuition fees.

A Research Development Grant of £5,000 is also available for each candidate to support advanced research training for the duration of the studentship.

How to Apply

Deadline: 16th February 2018

Candidates are asked to complete and return the application form available from:

Completed applications should be returned to the address specified on the application form.

Prospective candidates are strongly advised to contact Dr Neil Strevett and/or Dr Huw Millward for an informal discussion before submitting their application.

Dr Neil Strevett: Tel: 02920 375053

Dr Huw Millward: Tel: 01792 346245

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