"Postdoctoral Research Associate or Fellow: Epidemiological Modeller"

The Faculty wishes to recruit a Postdoctoral Research Fellow to participate in a 5-year, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in an intensively monitored wildlife disease system, with real-world applications for UK agriculture and conservation science. This NERC-funded post is available from 1st January 2026 to 31st December 2031. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark-recapture for demography and social network structure; genome re-sequencing for pedigree information and genomic prediction; diagnostic testing for infection status and prevalence of bTB. The Research Fellow will join a team of five postdoctoral researchers, research faculty from Universities of Exeter, Sheffield and Edinburgh, and project partners from the Animal and Plant Health Agency, DEFRA and the Office for Environmental Protection.

The post will include the design, development and translation of Bayesian epidemiological compartmental models, using large and long-term datasets to support their inferences. The Fellow will work with team members to develop models to quantify evidence regarding the effects of social, demographic, age-dependent, kin-related and genetic drivers of susceptibility to infection and of onward transmission of infection, among host individuals. The Fellow will be responsible for coordination of collaborative epidemiological research using these models, and for leading primary research outputs, presenting findings at conferences and workshops, and engaging in impact-related activities with stakeholders.

Applicants will possess a relevant PhD or equivalent qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in the discipline to develop research methodologies. The successful applicant will be able to work collaboratively, supervise the work of others and act as team leader as required. Applicants will have excellent written and verbal communication skills, experience with developing and implementing Bayesian statistical models, and be proficient in computer programming in e.g. R or Python, and C/C++.