Senior Postdoctoral Research Scientist in AI for Outbreak Response Full-time NEW

Senior Postdoctoral Research Scientist in AI for Outbreak Response

Pandemic Sciences Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF Additionally, Pandemic Sciences Institute, Life and Minds Building, South Parks Road, Oxford, OX1 3EL

We are seeking to appoint a Senior Postdoctoral Research Scientist in AI for Outbreak Response to join the Data Analytics and Epidemiology group at the Pandemic Sciences Institute. Working with Professor Moritz Kraemer, you will contribute to ORCAS (Outbreak Response and Control with AI Support), an international consortium developing AI-driven tools to enable more timely and cost-effective responses to infectious disease outbreaks in Nigeria and the Democratic Republic of the Congo. The research will focus on disease forecasting, scenario modelling, and optimal decision making for public health interventions and surveillance.

You will be responsible for developing and validating machine learning models for disease forecasting and simulation models for scenario exploration, utilising large-scale data across a range of modalities (e.g., epidemiological, genomic, behavioural). You will contribute to the development of decision-support tools that translate model outputs into actionable insights, and the integration of these tools into existing public health workflows. You will also work closely with public health partners in Nigeria and the DRC to understand their needs, adapt models to local contexts, and support their use through co-design workshops and training.

It is essential that you hold a PhD/DPhil with post-qualification research experience in a relevant quantitative discipline, such as epidemiology, statistics, or computational biology. You should have experience developing and deploying machine learning models for disease forecasting and scenario modelling, strong proficiency in Python, R, and/or other programming languages, and experience building reproducible computational pipelines and software. Experience analysing large-scale data and evaluating machine learning models with uncertainty quantification are also essential. Experience with reinforcement learning, phylogenetics/phylodynamics, and developing user-facing dashboards or visualisations to communicate complex scientific findings to non-technical audience would also be advantageous.

Applications for this vacancy should be made online and you will need to upload a supporting statement and CV. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. Please restrict your documentation to your CV and supporting statement only. Any other documents will be requested at a later date.

This position is offered full time on a fixed term contract until 31 March 2028 and is funded by The Gates Foundation. Only applications received before 12 midday on 9 November 2026 will be considered. Please quote 189012 on all correspondence.