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Mathematical modelling of cancer immunology

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  • Full or part time
    Dr B MacArthur
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Cancer immunology aims to enable the body’s own immune system to recognise and kill cancer cells. Such treatments are tremendously exciting because they are potentially able not only to destroy visible cancer cells, but also seek out and eradicate hidden cancers in other parts of the body and provide our bodies with a lifetime of immunity. However, the mechanisms by which tumour cells evade the immune response are not well understood. To address this issue, this project aims to combine practical experimentation with new mathematical models of tumour-immune cell interactions, in order to better understand the mechanistic basis of cancer immunology, and help develop the next generation of cancer immunotherapy.

As part of the newly established Centre for Cancer Immunology you will be working in the UK’s leading community of cancer immunologists, as well as collaborating with a wide range of scientists from other disciplines – including mathematicians, statisticians, computer scientists and clinicians – with the aim of using immunotherapy to eradicate cancer. Our exciting discoveries are already moving out of the laboratory into clinical trials, where they are making a significant difference to people with cancer. But there is much more to do, and your research, as part of this project, could help establish the next generation of cancer treatments.

This is a challenging project, which will involve integrating mechanistic mathematical models with advances in machine learning in order to better understand the complex data produced from experiment. Applicants should accordingly have a first class degree or MSc (or equivalent) in the mathematical sciences or a related discipline with a large quantitative component such as physics or statistics. Applicants with a demonstrated interest in the use and development of quantitative methods in the life sciences and/or expertise in immunology or cancer biology will be particularly welcome. The project will be co-supervised between the Departments of Mathematical Sciences and Medicine and so applicants must have strong interest in and motivation for interdisciplinary research. The ability to communicate effectively across discipline boundaries is essential.

Southampton has a distinguished history of immunotherapy which started in the 1970s and now boasts more than 150 staff focused on this aspect of cancer research. One of the major distinguishing features of the programme of work at the University of Southampton is the distinct translational element – right now we are leading the development of five new immunotherapy drugs which all originated from the Southampton labs. In 2017 we will open the UK’s first dedicated Centre for Cancer Immunology, including 4000m2 of new labs and offices that will house facilities to support the invention and deployment of new cancer immunotherapies, from their discovery in the laboratory, via preclinical modelling, to first-in-human clinical trials and on to wider multicentre trials.
For more details see: http://www.southampton.ac.uk/youreit/#intro-section


Prospective candidates are required to apply for the MPhil/PhD in Mathematics using the University of Southampton on-line application system, and include the name of this project and lead supervisor (Ben MacArthur) where prompted.

A copy of the on-line application form and guidance notes can be found here: http://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further enquires contact Dr Ben MacArthur ([email protected]) or Prof Tim Elliott ([email protected])

More information on the project supervisors and their research can be found here:


Related Subjects

How good is research at University of Southampton in Mathematical Sciences?

FTE Category A staff submitted: 54.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities
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