We’re offering an exciting studentship here at the MRC/CSO Social and Public Health Sciences Unit at the University of Glasgow, focussing on the application of machine- and deep-learning methods to sensitivity analysis for complex agent-based models. The full details and application information are available here: https://www.findaphd.com/search/ProjectDetails.aspx?PJID=102756
Population health research has made significant strides in recent decades, but some health challenges facing society remain very difficult to study. Issues like the ageing population, increasing obesity, and multi-morbidity are driven not by simple cause-and-effect relationships, but are influenced by behavioural, environmental and social factors. Increasingly, we are turning toward interdisciplinary computational modelling techniques such as agent-based modelling (ABM) to unravel the complex interplay of factors that drive these urgent problems in population health. ABMs are computer simulations that model the behaviours of individual people in complex virtual environments, and consequently help us better understand how the interaction of the individual, the environment, and the social realm lead to poor health.
This project will tackle this exciting area of research head-on by applying ABM to key problems in population health, and using cutting-edge AI techniques to better understand the behaviour of these complex models. Machine- and deep-learning methods can improve the theoretical understanding of the ABM, help calibrate the model, and facilitate interpretation of the results relevant to end users. The PhD candidate will develop novel frameworks for the analysis of large simulation models using machine learning and deep neural networks. These innovations will facilitate the development of new techniques, protocols and software for the analysis and dissemination of complex simulation studies in population health, opening up new avenues of research on major health challenges.