Research fellow in modelling vaccine impacts
Big Data Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF

We are seeking to appoint a Research Fellow in Modelling Vaccine Impacts to join Professor Deirdre Hollingsworth’s team at the Centre for Tropical Medicine and Global Health.

As a Research Fellow, you will be leading research in optimising the use of vaccines for Lassa fever and Chikungunya in low-income populations. You will be primarily responsible for considering the dynamics of Chikungunya transmission, and will also be involved in optimising vaccine allocation for multiple diseases. This post would be suitable for someone entering infectious disease modelling from mathematics, computing, or statistics, as well as for an experienced infectious disease modeller who wants to broaden their expertise and do policy-facing work.

It is essential that you hold a PhD/Dphil (or close to completion) in a quantitative research area such as mathematical modelling, computing or statistics, together with relevant experience. Sufficient specialist knowledge in the discipline to work within established research programmes in infectious disease modelling or optimisation is required, alongside strong analytical and quantitative skills and excellent problem-solving abilities. The ability to manage own academic research and associated activities and independently lead research, to work collaboratively with others and to work to deadlines is also required.

Applications for this vacancy are to be made online and you will be required to upload a supporting statement and CV as part of your online application. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience.

This position is offered full time (part-time may be considered at a minimum of 22.5h / 0.60FTE) on a fixed term contract until 30 June 2024 and is funded by the Li Ka Shing Foundation.

Only applications received before 12 midday on Tuesday 7 November 2023 will be considered. Please quote 167051 on all correspondence.