The COVID-19 pandemic has demonstrated the urgent need for improved preparedness to control the outbreak of emerging respiratory virus infections. The overall aim of the project will be the development of mathematical and computational tools to improve understanding of i) the replication of respiratory viruses, such as SARS-CoV-2 and influenza, within an individual host, ii) the transmission of viral infections between individuals in highly heterogeneous environments. The tools will be employed to facilitate the development and assessment of novel prophylactic treatments for the prevention of pathogenic respiratory virus infections.

The focus will be on the development of a novel class of within-host models of viral dynamics that would address the challenges of model parametrisation given the available data, improve the description of the dynamical changes of viral load in different risk groups, and generate insights into the impact of antivirals in combination of that of the immune response. The within-host model will be linked to i) a clinical trial simulation, an essential tool in drug development, to inform the clinical trial design prior to the conduct of the actual clinical studies, ii) an individual-based transmission model of disease dynamics that will be used to assess the potential impact of treatments on controlling the transmission of emerging respiratory infections in the community. As part of this, the impact of the heterogeneity in the structure of the individuals contact network on disease transmission will be investigated. The models will be calibrated within a Bayesian framework using the available clinical and epidemiological data.

The candidate will gain experience in a range of mathematical and computational techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference, analysis of clinical and epidemiological data.

The candidate will have the opportunity to plan, carry out and lead high-quality research in the area of infectious diseases, but also be involved in other research activities of the group. They may also have the chance to work with leading researchers from other academic institutions around the world, as well as industrial partners. This PhD project provides an excellent opportunity to develop state-of-the-art mathematical models and computational tools of emerging respiratory pathogens, within a world-renowned research group.

The Mathematics applied to Biology Group undertakes leading interdisciplinary research on infectious diseases, using approaches ranging from mathematical modelling of viral kinetics, through the modelling of disease transmission dynamics and control, to the simulation of clinical trials of potential treatments. The Department of Mathematics at Sussex offers excellent research facilities and a friendly, intellectually stimulating working environment.

Funding
• Fully-paid tuition fees for three and a half years.

• A tax-free bursary for living costs for three and a half years (£17,668 per annum in 2024/25).

• Additional financial support is provided to cover short-term and long-term travel.

• If you are not a UK national, nor an EU national with UK settled/pre-settled status, you will need to apply for a student study visa before admission.

Eligibility
Applicants must hold, or expect to hold, at least a UK upper second-class degree (or non-UK equivalent qualification) in Mathematics, Statistics, Physics, Computer Science, Bioinformatics, Epidemiology, or a closely-related area, or else a lower second-class degree followed by a relevant Master's degree. They must have a strong background in mathematical modelling and an interest in infectious disease epidemiology and mathematical modelling in Biology and Medicine. Experience in parameter estimation, knowledge of Bayesian methods and computer programming skills would be an advantage. Good communication skills are also essential.
This award is open to UK and International students.

How to apply
Apply through the University of Sussex on-line system.
https://www.sussex.ac.uk/study/phd/apply/log-into-account
Select the PhD in Mathematics, with an entry date of January 2025, May 2025 or September 2025.
In the Finance & Fees section, state that you wish to be considered for studentship no MAB/2025/01.

We advise early application as the position will be filled as soon as a suitable applicant can be found.
Due to the high volume of applications received, you may only hear from us if your application is successful.