"Research Associate in Deep Learning for Infectious Disease Modelling"

About the role

The post is funded by the U.S. National Institutes of Health (NIH) to develop and apply deep learning methods for modelling the spread of antimalarial drug resistance in sub-Saharan Africa.

Malaria drug resistance poses a major and growing threat to global malaria control, yet the mathematical models needed to understand and predict the emergence and spread of resistance are often too complex to fit directly to data. Recent advances in artificial intelligence, particularly deep learning–based surrogate models, offer a transformative opportunity to overcome these computational barriers, enabling scalable inference and prediction from rich genomic and epidemiological datasets.

What you would be doing

The post holder will work on the development of deep learning surrogate models that emulate complex malaria transmission and genetic models, allowing efficient Bayesian inference and forecasting across space and time.

Based at Imperial College London, the post holder will work within a highly interdisciplinary international team spanning machine learning, statistics, genomics, epidemiology, and geography, and will contribute to methodological advances at the interface of AI and infectious disease modelling with direct relevance to public health decision-making.

What we are looking for

• Hold a PhD in machine learning, computer science, statistics, applied mathematics, data science, computational epidemiology, or a closely related quantitative discipline (or equivalent research, industrial, or commercial experience) Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.
• Demonstrated research experience in deep learning, evidenced by peer-reviewed publications, preprints, open-source software contributions, or equivalent research outputs.
• Practical experience developing, training, and evaluating deep neural networks.
• Experience developing reproducible ML pipelines (e.g. experiment tracking, version control, structured workflows).
• Strong knowledge of deep learning principles, including neural network architectures, optimisation, and regularisation.

What we can offer you

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further Information

This role is for a full-time and fixed-term contract for 4 years.

If you require any further details about the role, please contact: Dr Robert Verity – r.verity@imperial.ac.uk.