Coupling homogeneous population infection risk models with high-fidelity human behaviour predictions

The COVID-19 pandemic has spawned an exponential growth in research that links mathematical models with an engineering and social context. One of the biggest unanswered question is how to link population level disease spread models with high fidelity ones of how individuals behave within buildings. 

You would develop connections between engineering, computing and mathematics to study human behaviour modelling and risk in indoor environments. You would create new methodology for agent-based models predicting people touching surfaces with infection risk models potentially through machine learning and observational studies. This could then be validated through surface-to-finger microbial transfer experiments.

Your work could have potential for policy recommendation where you combine the human behaviour prediction and microorganisms transfer models with population level risk models. This is an opportunity to work with a multidisciplinary team of mathematicians, engineers, microbiologists and behavioural scientists as well as external partners in the NHS and the UK’s Defence Science and Technology Laboratory (DSTL).

A strong analytical background with either a degree in: mathematics, computing, physics or similar.