Summary:
Imperial College London is seeking a machine learning researcher dedicated to addressing challenges in global health through deep generative modelling techniques. Join a collaborative international team to develop scalable tools for understanding infectious diseases.
Research Associate in Deep Generative Modelling for Infectious Diseases, Imperial College London
Designation: Research Associate
| Field | Details |
|---|---|
| Research Area | Deep Learning, Infectious Disease Modelling |
| Location | White City Campus – Hybrid |
| Salary Range | £49,017 – £57,472 per annum |
| Contract Type | Full-time, Fixed term |
| Posting End Date | 1 July 2026 |
| Start Date | 1 September 2026 |
| Contract End Date | 31 August 2028 |
Eligibility/Qualification:
- PhD in machine learning, statistics, applied mathematics, computer science, or a closely related quantitative discipline.
- Demonstrated research experience in deep learning and probabilistic machine learning, evidenced by publications or open-source contributions.
- Practical experience in designing, training, and evaluating deep generative models.
- Strong programming skills in Python, with proficiency in PyTorch or JAX.
- Ability to adapt methods for novel scientific applications and communicate across disciplines.
Job Description:
As a Research Associate, you will:
- Lead methodological development in deep generative modelling, simulation-based inference, and spatial Bayesian inference.
- Develop scalable tools to address computational challenges in fitting complex disease models to data, particularly focusing on antimalarial drug resistance in sub-Saharan Africa.
- Collaborate with an interdisciplinary team spanning machine learning, statistics, genomics, and epidemiology.
- Explore rigorous methodological innovations with real-world applications in global health.
How to Apply:
Please submit your application via the Imperial College London job portal. For inquiries, contact Dr. Elizaveta Semenova at e.semenova@imperial.ac.uk.
Last Date to Apply: 1 July 2026
Join us at Imperial College London to make a significant impact in global health through innovative machine learning solutions!
