Respiratory virus transmission is highly heterogeneous and seasonal. Transmission dynamics are known or hypothesised to be driven by complex spatio-temporal factors such as co-circulating respiratory viruses, population density, host mobility, age, comorbidities, immunity from natural infection and/or vaccination, climate, pollution. A better understanding of the interactions between such drivers and respiratory viruses is necessary to optimise the surveillance and control of endemic and emerging respiratory infections.

We are seeking a candidate for a post-doctoral position with an expertise in statistics, data analytics, or mathematical modelling of infectious diseases. The candidate will be able to pursue different research directions according to their technical skills and research interests:

• Analyse the spatio-temporal correlation in time series of respiratory infections and socio-demographic data
• Quantify the level of positive or negative interference between co-circulating respiratory infections
• Estimate the impact of control interventions aimed at one respiratory virus on the transmission of other respiratory viruses
• Predict the co-circulation of respiratory viruses based on epidemiological and socio-demographic data

Key responsibilities:

• Analyse epidemiological and socio-demographic data
• Develop statistical and/or mathematical models to characterise drivers of heterogeneous transmission of respiratory infections
• Develop statistical and/or mathematical models to analyse hypotheses on the interaction of different co-circulating respiratory infections
• Disseminate research results in peer-reviewed academic papers and at scientific conferences

Essential requirements:

• A PhD in a quantitative subject such as epidemiology, applied mathematics, physics, statistics, population biology
• Experience in spatial/spatio-temporal or time series analysis
• Experience coding in R or Python
• An interest in infectious diseases and public health
• Strong communication and writing skills
• Ability to work independently but also as part of a team

Desirable skills:

• Research experience in mathematical/statistical modelling of infectious diseases
• Familiarity with Bayesian inference methods and their implementations (e.g. manual implementation, Stan, INLA)
• Familiarity with high-performance computing

Location:
Pathogenesis and Control of Chronic and Emerging Infections (PCCEI), Montpellier University, 60 Rue de Navacelles, 34394 Montpellier, France

Application procedure:
For initial consideration for this position and more detail about the proposed project, please send an e-mail including your CV and a brief statement of interest to Constanze Ciavarella (constanze.ciavarella@umontpellier.fr) and Mircea T. Sofonea (mircea.sofonea@umontpellier.fr). The contact information of two academic referees will be required at a later stage of the application process.

The starting date is flexible, with funding secured until at least November 2026.

Review of applications starts immediately and will stop as soon as the position is filled.