PhD student

Human behaviour during epidemics reflects our nature as socially embedded decision-makers who interpret health risks through multiple, often competing, social identities. Traditional epidemic models offer mathematical precision but fail when confronted with this reality: people do not follow mathematical rules, they navigate health decisions as group members whose identities shape how they interpret risk, trust information, and act.
The PhD project will play a crucial role as part of a team translating psychological constructs into mathematical/dynamical rules driving comprehensive agent-based
models incorporating disease transmission, behaviour and feedback loops. Their first task will be to conduct a comprehensive literature review identifying epidemic-relevant social identities, their associated norms, contextual salience triggers, and within- versus across-group influence patterns. Working with datasets from the UK, Netherlands, and USA, the PhD student will produce the identity profiles-archetypal agent configurations capturing key behavioural heterogeneity-that form the foundation of the modelling platform.
In the second phase, the PhD student will collaborate with others in the grant team, working closely with the postdoctoral researcher, to translate these psychological constructs into formal computational rules suitable for agent-based modelling. The PhD student will ensure that formalisations remain faithful to the underlying psychological theory-a translational role that is central to the project's interdisciplinary ambition.
The PhD student will be jointly supervised by John Drury (social psychology, Sussex) and Marijn Stok (health psychology, Utrecht) with input from István Zoltán Kiss. The student will benefit from complementary expertise spanning the social identity approach, health behaviour research, and real-world policy advisory experience from COVID-19. The PhD student will take part in regular meeting with the entire team both online and in person.

Candidates should have a background in psychology, including some training in coding (e.g., R or Python). Knowledge of the social identity approach and health psychology models are essential, and experience of network science or computational modelling is desirable.