Developing an in silico method for endodontic restoration of human molar teeth

The proposed PhD project seeks to address the complex, multi-factorial issue of dental caries and its implications for endodontic treatment. With approximately 2.4 billion people affected worldwide annually, the need for effective interventions, such as root canal treatment, is evident. However, despite the widespread prevalence of post-treatment inflammation and infection, particularly in permanent molars, the underlying causes remain inadequately understood with substantial variation within individuals that is compounded by health inequalities. Consequently, there is a pressing need for research into personalised approaches to dental caries management and endodontic care.

To tackle this challenge, the project aims to generate a comprehensive dataset comprising experimental and imaging data of human molar teeth. This dataset will serve as the foundation for developing subject-specific computational models, enabling researchers and practitioners to investigate how variations in individual characteristics, such as tooth morphology and material properties, influence the efficacy of different root canal restoration approaches. Research objectives include:

•         Systematic review of the literature on experimental and computational methods and data in the field of endodontic treatments.

•         Designing experiments for mechanical investigations of endodontically treated molar teeth.

•         Development of in silico models of the endodontically treated teeth

The student will be exposed to a multidisciplinary environment that combines engineering, material science, and dentistry. The student will work in an inclusive environment with academics and clinicians to gain expertise in hypothesis testing through experimental design, imaging techniques for computational model development, and advanced computational modelling and validation techniques. The student will collaborate with stakeholders, including dentists and dental technicians, to promote the integration of computational methods into dental practice, thereby fostering wider adoption and application of in silico approaches. Through these endeavours, the project aims to contribute significantly to advancing personalised endodontic treatments and improving patient treatment outcomes.

Applicant Eligibility and Training

We are looking for candidates who have, or are due to obtain, a high 2.1 or 1st-class degree in an appropriate field of Engineering, Physics, Mathematics or Biomechanics.

Training will be provided throughout the study in several ways. The supervisory team and colleagues will provide project-specific hands-on training as needed and will follow a thorough Development Needs Analysis. This will include lab inductions, health and safety training, seminars, outreach opportunities, and journal clubs. As a member of the Liverpool Doctoral College, a wide range of additional training resources will be available. The student will have regular (at least monthly) formal meetings with the supervisory team.

Application Process

Candidates wishing to apply should complete the University of Liverpool application form [How to apply for a PhD - University of Liverpool] applying for a PhD in Materials Engineering and upload Degree Certificates & Transcripts, an up-to-date CV, a cover letter/personal statement and two academic references.

We want all our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations, including supporting those with caring responsibilities, disabilities, or other personal circumstances. We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.

Enquiries

Candidates wishing to discuss the research project should contact the primary supervisor, Dr Rosti Readioff [rosti.readioff@liverpool.ac.uk]. Those wishing to discuss the application process should contact the School Postgraduate Research Office [soepgr@liverpool.ac.uk].