Supervisor 1 Dr Gibin Powathil (Mathematics, College of Science)
Supervisor 2 Professor P Nithiarasu (College of Engineering)
Mathematical Oncology is currently an emerging and vibrant research area where we use mathematical and computational tools to study various aspects of cancer growth, invasion and associated multimodality treatments with an aim of providing better, individualised treatments to the patients.
The multiscale complexity of cancer progression warrants a multiscale modelling approach to produce truly predictive mathematical models. In order to capture all the dynamics of cancer progression, we need to couple processes that are occurring at various spatial and temporal scales. This studentship aims to develop and analyse multiscale 3D computational and mathematical models for cancer growth and spread, incorporating details of the various spatial and temporal scales involved in the growth and progression of solid tumours to provide an optimal patient-specific treatment protocol. We will study multiple anti-cancer therapeutic strategies such as chemotherapy, radiotherapy and immunotherapy by analysing their effects in tumour control and in the patient survival probability. The goal is to implement the models in a user-friendly framework to assist the clinicians in devising an optimal treatment strategy.
The process of developing a clinically relevant model requires careful formulation and involves collaborative modelling and clinical partnerships to account for the relevant mechanisms involved in various spatial and temporal levels of cancer growth. This is a multidisciplinary project with national and international collaborative partnerships from clinical, computational and experimental researchers. For more information on this project, please visit:
The successful candidate will be expected to commence their studentship on 1 July 2016 or 1 October 2016 at the latest.
Candidates must have an upper second class honours degree at undergraduate level or a Masters degree (with Merit), in a relevant discipline.
Knowledge in cancer biology is not essential but the candidate will be required to learn appropriate biological and clinical knowledge in order to develop predictive mathematical and computational models. The candidate is expected to have a strong applied mathematical background. Strong computational and programming skills in one or more programming languages such as C, C++ and Matlab are preferred. Strong interpersonal skills and the capacity to work and learn independently will be required.
Due to funding restrictions, this studentship is open to UK/EU candidates only.