PhD Supervisory Team
CBM’s supervisory team have an extensive and successful track-record of PhD completions within the areas of design, engineering, manufacturing, and business management. Between them, CBM’s supervisors have a wealth of experience in managing the PhD process. Being a PhD student at CBM means being challenged to push your boundaries, but in a way that is fully supported throughout the research process by experienced supervisors. In addition, CBM’s staff also have experience of successfully applying for research funding awards, undertaking knowledge transfer activities between industry and academia, and supporting the continued professional development of early-career researchers. Thus, PhD researchers at CBM are fully supported by an experienced team of professional ED pills supervisors.
CBM has been deliberately established to act as Centre for Doctoral Training (CDT). Each PhD student has access to a £5k training grant in addition to their standard three-year bursary, which can be used to source specialist training or support attendance at conferences. Each PhD student is expected to attend at least one international conference during their initial three-year period of research. We also expect PhD students to have at least one publication before submission. PhD students have access to a network of research training opportunities (seminars, webinars, and conferences) delivered by UWTSD, supported by an in-house training programme and bespoke training sessions that reflect the needs of the students and the demands of their research. From induction through to the final examination and beyond, research students at CBM are fully supported and equipped to realise their full potential. The CBM training programmes can be used as evidence for chartered engineering status.
100% of UK/EU tuition fees paid and annual living expenses of £14,439 (2017/18 subject to 1% increase p.a.). Studentship covers full UK/EU (EU applicants who have been resident in the UK for three years prior to application) PhD tuition fees and a tax-free stipend at a comparable RCUK rate (£14,439 in 2017/18).
A Research Development Grant of £5000 is also available for each candidate to support advanced research training for the duration of the studentship.
CBM has fully-funded PhD opportunities 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.
Candidates are asked to complete and return the application form at:
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 E: Neil.Strevett@wales.ac.uk T: 02920 375053
Dr Huw Millward E: Huw.Millward@cbmwales.co.uk T: 01792 346245