MARS Senior Research Associate in Machine Learning for Infectious Disease Models - 0308-26

MARS Senior Research Associate in Machine Learning for Infectious Disease Models

School of Mathematical Sciences

Location: Bailrigg, Lancaster, UK

Salary: £39,906 to £46,049 (Full-Time, Indefinite with End Date)

Closing Date: Sunday 17 May 2026

Interview Date: Wednesday 03 June 2026

Reference: 0308-26

MARS: Mathematics for AI in Real-world Systems is seeking a highly motivated and creative Senior Research Associate to join our interdisciplinary team at the frontier of computational epidemiology and machine learning. This role focuses on developing next-generation frameworks to predict, understand, and mitigate the spread of infectious diseases.

You will lead research in one (or both) of the following cutting-edge areas:

• Generative Inference and Monte Carlo Optimisation: Developing new generative machine learning approaches to improve the efficiency of high-dimensional Monte Carlo algorithms for stochastic epidemic models. Research directions may include discrete normalising flows, diffusion-based methods, online reinforcement learning methods, and amortised inference. The aim is to solve one of the last remaining barriers to successful disease modelling at scale, delivering faster and more reliable inference, better-calibrated predictive uncertainty, and computational tools for large-scale mechanistic models.

• Probabilistic Modelling of Higher-Order Contact Structure: Developing novel machine learning and statistical methodology for latent relational structure in populations, including higher-order, group-based, and temporally evolving interactions. Directions may include probabilistic graph and hypergraph models, generative approaches to large-scale contact networks, learning from partial or aggregate observations, and principled uncertainty quantification. The goal is to build scalable methods for inference and intervention-aware analysis in complex epidemic systems, with applications to targeted intervention design in settings such as schools, workplaces, and hospitality.

Key Responsibilities

• Develop and implement novel ML architectures and computationally intensive statistical methodology tailored to outbreak datasets.
• Collaborate with public health stakeholders and data providers to ensure models are grounded in real-world contact patterns.
• Publish findings in high-impact journals (e.g., Nature Communications, Lancet Digital Health) and top-tier ML conferences (NeurIPS, ICML, ICLR).
• Contribute to an open-source codebase to ensure reproducibility and utility for the wider scientific community.

You will work within a vibrant community of infectious disease modellers, centred in MARS, with collaboration across Lancaster Medical School, University of St Andrews and Liverpool School of Tropical Medicine.

This is a full-time, fixed-term position until 31 July 2029. Flexible working arrangements will be considered, but you will be expected to be present on the Lancaster campus a minimum of two days a week.

Contact: Professor Chris Jewell, c.jewell@lancaster.ac.uk or Dr Jess Bridgen, j.bridgen@lancaster.ac.uk

Why join MARS?

It is an exciting time to be part of MARS, which is based in one of the top-ranked maths departments in the UK. You’ll be part of a thriving and collegiate research group with a growing complement of academic staff, researchers and PhD students, and part of the next generation of mathematicians tackling real-world problems.

Equal Opportunities

Lancaster University promotes equality of opportunity and diversity within the workplace. For these positions, we welcome applications from all diversity groups, particularly from women who are currently underrepresented in the mathematical sciences. We warmly welcome applicants from all sections of the community regardless of age, religion, gender identity or expression, race, disability or sexual orientation.

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