MRC DiMeN Doctoral Training Partnership: Innovative mathematical approaches for the analysis of ex-vivo and in-vivo imaging in the drug discovery and development process


This is an opportunity to undertake one of our new and exciting cross-disciplinary projects lying at the interface of mathematics and cancer medicine. The candidate does not need to have any knowledge in cancer diseases or medicine, but will need strong mathematical knowledge through a degree in maths, or in computer science / physics / engineering with essential maths components, as well as good programming skills.

We have a number of different problems to be investigated and the projects intend to develop both new models, theories and industrial applications. The broader research areas to be employed include mathematical modelling & optimisation, machine learning & data analytics, as well as algorithms and statistical analysis methods. Examples of such techniques include convexity, nonlinear and constrained optimisation, primal-dual, scale-space approaches, relaxation methods, neural networks, deep and reinforcement learning, statistical, semi-unsupervised and supervised machine learning, image processing, large-scale data visualisation, object sequencing, computational geometry, and any other methods that are potentially useful to solve the problems at hand.

The student will work closely with our very strong teams of mathematicians, preclinical scientists, top Astra Zeneca researchers, medical doctors to develop ways of segmenting 3D images and tracking 3D volumetric changes in in-vivo images and for alignment with ex vivo images for optimised experimental designs in safety studies for new therapies geared towards oncology targets. The supervisory team has a strong track record in the defining ingredients of the underlying work and will closely contribute to the originality of the research. Supervision is provided by three experienced supervisors. Publications in top-tier theoretical and also application-oriented journals will be expected. The 42-month PhD project will tackle multidisciplinary problems co-defined by three supervisors. Core training in mathematics, data science, etc., will form part of a unifying curriculum, together with leadership and entrepreneurship training, to underpin the individual research projects.

Background: A critical step in the drug discovery and validation process pertains to the evaluation of safety and efficacy profiles for candidate molecules. The industrial partner AZ is well-known for leading such development work and excellent achievements. Image analysis is a key component of this evaluation process which employs a standard procedure of rigorous tests and trials. Hence the images themselves from this procedure are considered given for this project.

This discovery process is non-trivial because one cannot assume that all annotations are done and standard AI approaches will work unless it is a simple problem to study. To the contrary, new mathematical models must be developed to generate precision predictions that will be refined by validations and designed experiments. AI will be an assistive tool rather than the solution. Integration of these large historical datasets into a single model and an analysis pipeline is challenging. Mathematical modelling, optimisation and machine learning methods have been shown to be promising in some complex problems and recent work has demonstrated that such methods. This project will be developing algorithms first for accurate segmentation of 3D volumes in in-vivo images derived from drug validation and safety studies and then for accurate tracking of 3D volumetric changes in in-vivo images and for alignment with ex vivo images for optimised experimental designs. The attractiveness of this exciting project lies not only in involving advanced mathematics (optimization and variational models) in a rare opportunity with a leading pharmaceutical company, but also in providing automation tools and assistive technologies directly to new therapies in pharmaceutical settings, not just potentially useful.

 The candidate should have at least a 2.1 BSc in Mathematics or related discipline with maths components (ideally M.Math or MSc in Maths or related) and also be competent in scientific programming (Matlab, Python, or C++).

Informal enquiries should be addressed to Prof Ke Chen - [Email , Web ].

Tel. No. for Enquiries: +44 (0)151 794 4741

Benefits of being in the DiMeN DTP:

This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle, York and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.

We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.

Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here:

Further information on the programme and how to apply can be found on our website:


Open to students worldwide

Funding information

Funded studentship

iCASE Award: Industrial partnership project
Funded by the MRC for 4yrs, including a minimum of 3 months working within the industry partner.

Funding will cover UK tuition fees and an enhanced stipend (around £18,109). We also aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will be awarded to exceptional candidates only, due to the competitive nature of this scheme. Please read additional guidance here: View Website
Studentships commence: 1st October 2022
Good luck!