A master’s degree in mathematics, statistics, epidemiology, or an equivalent degree in a subject with a strong analytical and/or public health component. Evidence of prior interest in the fields of global health and epidemiology. Evidence of substantial experience with programming in R, Python or similar. Evidence of understanding of the basic concepts of mathematical modelling for infectious diseases as well as of epidemiological measures. Applicants ideally will also have: Experience with the implementation and interpretation of mathematical models for infectious diseases. Experience with fitting models to data Experience in the use of version control and collaborative coding (e.g. via github) Knowledge of RSV epidemiology Knowledge of vaccinology of childhood vaccines for respiratory pathogens

In the last few years many new potential strategies for the prevention of RSV, the main cause of childhood pneumonia globally, have become available, with more in the clinical development pipeline. The successful candidate will use mathematical models and statistical modelling, combined with health economics to evaluate the relative merits of the available products in a fast-changing landscape. This will include analyses to further our understanding of RSV transmission dynamics and/or seasonality. The scope of work has some flexibility and can be adapted in accordance with the successful candidate’s skills and interests. While the position requires a substantial background in programming and infectious disease analytics, there will be the opportunity for additional training. The successful candidate will also be expected to regularly attend scientific conferences and collaborative visits. The successful candidate will come with a high degree of motivation and self sufficiency and will be expected to work interdisciplinary with multiple international collaborators both for research and public health policy including e