Self funded project October 2022 / January 2023 start, 3 / 3.5 years duration.
Cancer is the greatest cause of premature death in the UK, with around 370,000 new cases and 165,000 lives lost per annum. Alongside the human loss, cancer has a £1bn annual socio-economic impact, which will be replicated worldwide.
Early detection remains the most likely approach to maximise cancer survivorship. Whilst radiographic scanning capabilities continue to increase, the capability of the human operator to detect increasingly subtle tissue abnormalities is inherently limited. This means early-stage tumours may be missed, due to a lack of sensitive detection strategies.
Radiomics is an emerging field that integrates expansive computational power and artificial intelligence, unlocking new opportunities to detect and monitor ever-smaller anatomical and functional abnormalities via clinical imaging. Successfully exploiting this new technology has the potential to produce a step-change in early cancer detection capabilities and smart decision support systems; however, such advances need to be supported by parallel technologies to produce synthetic tumours, providing the ‘ground truth’ for validation.
This PhD will focus on developing these next-generation ‘phantoms’. It will adopt digital manufacturing and the associated design freedom to develop phantoms with tissue-equivalent textures and geometries. We will also leverage new, EPSRC-funded capabilities to precisely deposit unique material blends, to achieve tissue-equivalent radiographic behaviour. Our preliminary data indicates that we can achieve these outcomes with sub-millimetre accuracy and precision, which would represent a step-change in commercially available phantoms.
The PhD student will be immediately integrated into a thriving research community. This project represents a collaboration between the Medical Experimental, Design and Computational Lab (Dr Theobald) and the Cancer Imaging and Data Analytics team (Dr Spezi). They will become expert in the theory and practice of AM, driven by the need to achieve accuracy and precision using novel materials. They will gain exposure to scientific and clinical imaging/scanning, as routes to analyse build quality and to generate tissue-equivalent data.
Skills development is a key pillar of the research plan, reflecting the need to train the next generation of medical engineers in emerging scientific fields. The student will work closely with both research teams, ensuring they are exposed to aligned studies that enable development of wider knowledge and understanding. They will also be involved in discussions with clinical collaborators, to appreciate the end application. Conference attendance and presentation will also be integrated as a significant route to facilitating development of their own networks, a valuable foundation for a future research or academic career.
This studentship represents an opportunity to work across two leading research teams, harnessing EPSRC-funded additive manufacturing infrastructure to enable a potential step-change in cancer detection strategies. This will afford the student opportunity to become expert in emerging digital manufacturing technologies, which is highly valuable as the sector increasing operates with Industry 4.0.
The student will be focusing on appreciating the interaction between materials and their processing parameters, fundamental concepts that both underpin mechanical behaviours and offer opportunity to unlock innovative solutions. Becoming skilled and knowledgeable in how such solutions can function within a clinical-related context is of great value, given that this is a sector moving increasingly towards precision medicine.
The student will also benefit from close supervision by experienced academics, with diverse research backgrounds featuring a strong overlapping interest in medical-related activities. Inclusion in external collaborations, including international research partnerships and standards committees, will be supported and are intended to create valuable learning opportunities. Such an inclusive approach also creates an environment for integrative meetings and mentorship, working alongside PhD and post-doctoral colleagues to leverage peer-to-peer learning, adding significant value.
The PhD student will also be encouraged to engage fully with the Doctoral Academy, which offers a breadth of training and learning opportunities in both broad and subject-specific skills. Support will also be provided to attend training programmes to develop niche, project-related skills that may fall outside of the other activities.
This PhD studentship will kick-start a new research focus within ENGIN, integrating two emerging strengths from the Medical Engineering Research Group.
(or their equivalent) in Engineering or a related subject.
Desirable skills: (a) experience with one or more of the following platforms: MATLAB, Python, Dragonfly, Ultimaker Cura, (b) a passion for coding and using software, (c) attention to detail, (d) evidence of scientific publications in English, (e) basic knowledge of project management tools such as Gantt charts and SWOT analysis.
Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)
Applicants should submit an application for postgraduate study via the Cardiff University webpages (http://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering ) including;
· an upload of your CV
· a personal statement/covering letter
· two references (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school)
· Current academic transcripts
Applicants should select Doctor of Philosophy (Engineering), with a start date of July or October 2020.
In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding, reference PTES-SF-23
Deadline for applications 1st March 2023. We may however close this opportunity earlier if a suitable candidate is identified.
Please contact Dr Theobald (firstname.lastname@example.org) and Prof Spezi (email@example.com) to informally discuss this opportunity