The position requires a substantial background in programming and infectious disease analytics. The successful candidate will be expected to regularly attend scientific conferences and collaborative visits. The position requires a substantial background in programming and infectious disease analytics. The successful candidate will be expected to regularly attend scientific conferences and collaborative visits. To be considered applicants will need to have: A doctoral degree in mathematics, statistics, computer science, 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 experience in the use of AI and LLMs. Interest in working in inter-disciplinary teams. The successful candidate will likely also have: Experience with Bayesian statistics and/or math modelling. Experience in infectious disease research.

The successful candidate will be researching the potential use of AI and Large Language Models (LLMs) in outbreak response and develop useable pipelines. There will be an initial focus on the use of LLM-based agents to integrate R packages for data cleaning and the calculation of key epidemiological quantities included e.g. in with the Epiverse initiative. A key element of the research will be how such workflows can appropriately communicate uncertainty beyond the statistical uncertainty in the data. In addition, the successful candidate will also contribute towards/be a part of the WHO pandemic hub’s outbreak analytics when required. Towards the end of this fixed term position the postdoc will ideally provide or contribute towards training of (international) colleagues in the use of AI and LLMs assisted outbreak analytics.