An exciting opportunity to become a key member of the newly formed EpiEng group. This research group, led by Dr Parag, aims to discover how foundational principles from engineering and mathematics can be adapted and fused to devise new models and design novel pandemic prediction and control algorithms at scale. A central goal of the group is to probe the limits of what models can and cannot do.

As a Research Associate in Epidemic Modelling and Engineering you will explore the role that feedback loops (e.g., among interventions and behaviours of sub-groups of a population) and other circuits may play in driving observed pandemic dynamics and investigate how to exploit this knowledge to optimise the effectiveness of pandemic response. The post involves a mix of theory, programming and empirical application.

This role is ideal for someone with strong quantitative skills interested in confronting those skills on key challenges in pandemic modelling. Experience in epidemiology is not required and candidates with diverse academic backgrounds e.g., in statistics, data science, physics, engineering or mathematics, are welcome to apply.

This post is collaborative and you will engage with leading academics across Imperial College and the Universities of Oxford, Hong Kong, Liverpool and Lund. You will be supported to attend conferences and workshops to develop or broaden your interests and skillsets. Mentoring and flexibility will be prioritised to help design your research programme at the intersect of epidemiology and engineering.

This post is full time and fixed term for up to 3 years. It is held at the MRC Centre for Global Infectious Diseases of Imperial College London, which is a world leading institution for epidemic modelling. Flexible working will be considered, and details can be discussed at interview. The salary range is £43,093 – £50,834. Start dates are also flexible though there is a prioritisation towards winter.

Full job description at https://www.imperial.ac.uk/jobs/description/MED03369/research-associate/Final%2BJD%2B-%2BMED03369%2B-%2BGS.pdf. For more information email k.parag@imperial.ac.uk or tweet @krisparag1.