About the role:

Are you a modeller or data scientist ready to help reshape malaria surveillance in a changing world? As global funding cuts threaten both intervention delivery and the population-based surveys traditionally used to track malaria, countries urgently need sustainable, real-time alternatives. We are seeking a Research Associate in Malaria Surveillance Modelling and AI to help meet this challenge by transforming data provided by pregnant women attending antenatal care into actionable intelligence on malaria transmission and burden. You'll work alongside national malaria programmes to co-develop tools that are embedded into routine health systems and data dashboards used to guide policy decisions.

This role sits at the intersection of generative machine learning, statistical inference, geospatial analytics, and dynamical modelling — with a growing emphasis on adapting tools for new geographies and near-elimination contexts. It offers the opportunity to contribute to high-impact, policy-driven research while developing and deploying open-source tools that shape how malaria is tracked and tackled across sub-Saharan Africa.

What you would be doing:

You will design, implement and evaluate statistical and machine learning models that translate ANC malaria data into actionable estimates of transmission intensity and burden. This will include working with time-series and geospatial data, adapting existing modelling pipelines, and contributing to the development of new approaches for low-transmission and elimination settings.

You will contribute to building open-source tools (such as https://mrc-ide.github.io/anatembea/), integrating outputs into existing health information systems (e.g. DHIS2), and collaborating with national malaria control programmes to ensure the tools meet real-world needs. You will also support dissemination and training efforts, contribute to publications, and help supervise junior researchers as the project expands.

What we are looking for:

A PhD in a relevant quantitative discipline (e.g. mathematical modelling, geo-statistics, machine-learning)
Experience developing and applying statistical or machine learning models to real-world data
Strong coding skills (e.g. in R, Python, or C++) and familiarity with collaborative software development
Can work independently while contributing to a collaborative, cross-disciplinary team
Able to communicate technical ideas clearly to both technical and non-technical partners
Proactive, adaptable, and able to respond rapidly to the evolving needs of partners navigating a period of uncertainty, while also contributing to the development of robust, maintainable tools that support long-term surveillance capacity

What we can offer you:

The opportunity to contribute to a high-impact project at the forefront of global malaria surveillance innovation
A central role within a collaborative, interdisciplinary team working closely with national malaria programmes and global partners
Access to rich, policy-relevant datasets and established platforms (e.g. DHIS2) already integrated with national systems
Career development support, mentoring, and the chance to co-author high-impact publications and open-source tools
A world-class research environment at Imperial College London, with flexible working policies and sector-leading support for researchers

Further Information

This is a full-time post, fixed term for two years, with an immediate start date available and flexibility for the right candidate. The role is based at Imperial’s White City Campus within the School of Public Health, with options for hybrid working.

You will be part of the MRC Centre for Global Infectious Disease Analysis, a world-leading hub for infectious disease modelling and policy impact. For informal enquiries, please contact Dr. Patrick Walker – patrick.walker06@imperial.ac.uk.

£49,017 to £57,472 per annum

Imperial College London jobs

Research Associate in Malaria Surveillance Modelling and AI

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