Research Fellow in Epidemiological Modelling in Bats

About the role

We are looking for a motivated post-doctoral researcher to assess potential zoonotic disease transmission pathways between bats humans. As part of an international consortium, OneBAT-funded by Horizon Europe, you will collaborate with an interdisciplinary network of researchers working at the interface of ecology, epidemiology, and public health.

One focus of the Sussex team is to understand the circulation and persistence of zoonotic viruses within bat populations and the risks of spillover into other hosts. In this position, you will analyse a unique longitudinal dataset collected from maternity roosts in five European countries, integrating molecular and serological data to characterise infection dynamics of three major viral groups: lyssaviruses, filoviruses, and coronaviruses.

A central objective of the project is to quantify transmission dynamics within bat colonies in relation to the life-history of the bats and the co-circulating viruses. You will apply advanced statistical and mechanistic modelling approaches, including fitting compartmental (SIR-type) models to real-world surveillance data from wild animal populations. This will involve close collaboration with field ecologists and laboratory scientists across the consortium.

The role requires excellent quantitative and analytical skills, with strong experience in statistical modelling and programming in R. Experience with infectious disease modelling, particularly in wildlife systems, and the ability to handle complex longitudinal datasets will be highly advantageous.

You will contribute to advancing our understanding of how viruses persist and spread in natural host populations, with implications for predicting and mitigating zoonotic risks.

About you

You will have a PhD or equivalent experience in infectious disease modelling, together with strong quantitative skills and experience of fitting compartmental models to surveillance data. You will be able to interpret molecular and serological data, and have a high level of competency in R. Experience of working with large and complex datasets is essential, as is the ability to communicate research methods clearly to non-specialists such as volunteers or practitioners. A strong track record of effective teamwork and excellent interpersonal skills are also required.

Ideally, you will have a good understanding of infectious disease epidemiology, particularly in wild populations, and experience with ecological or epidemiological modelling approaches. Familiarity with programming languages such as R or Python, and experience developing efficient workflows for managing data-intensive sources, would be advantageous.