Project code



School of Mathematics and Physics 

Applications are invited for a fully-funded three year PhD to commence in October 2021. 

The PhD will be based in the School of Mathematics and Physics and will be supervised by Dr Marianna Cerasuolo, Dr Jamie Foster and Dr Andrew Burbanks

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables. 

The work on this project could involve:

  • Development and qualitative analysis of mathematical models for cancer dynamics and drug-cancer cells interaction
  • Data analysis
  • Computer programming
  • External collaboration with interdisciplinary teams

Prostate cancer (PCa) is the second most common cause of cancer among men worldwide.  Advances in screening and diagnosis have allowed detection of the disease in early stages. However, for late stage disseminated diseases current therapies are merely palliative.  Recent interdisciplinary studies emphasized the need to evaluate new therapeutic strategies to control PCa dynamics and the onset of drug-resistance; and current experimental evidence suggests that multi-drugs therapy is the way to succeed.  In the last few years, mathematical oncology has become increasingly important in supporting experimental studies to gain insights into cancer research and find new personalised therapeutic strategies to fight this deadly disease.

The aim of this project is to develop and validate continuum and hybrid mathematical models to gain insights into the interplay between newly discovered drugs and PCa cells. These tools will help understanding how the drug-cells interaction can affect the dynamics of the tumour growth and, at the same time, will allow to explore the effectiveness of different therapeutic strategies. In particular the project will focus on representing mathematically different-scales mechanisms that contribute towards drug resistance in cancer; cells metabolic changes during treatments and the impact of such changes on cancer behaviour (for example, development of metastasis).  The analytical and numerical study of the dynamical systems as well as the statistical comparison between numerical simulations and experimental data will also be part of the project.

Candidates will receive training in all relevant areas and have the opportunity to learn new skills in applied mathematics, cancer modelling, data analysis and computer programming. The student will also have access to a large number of training resources available through the Graduate School at the University of Portsmouth including those geared toward improving presentation skills, time-management, project organization skills, thesis writing, data analysis and statistics, and other various related training modules.

Entry reqs

You'll need a good first degree from an internationally recognised university (first class or equivalent, depending on your chosen course) or a Master’s degree in Mathematics, Physics, Engineering or Computer Science and have a genuine interest in Biomedical Sciences. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Previous research experience in applied mathematics, computational biology or computer programming is welcome.

We’d encourage you to contact Dr Marianna Cerasuolo ( to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

If you want to be considered for this funded PhD opportunity you must quote project code SMAP5970521 when applying.