The successful candidate will study the spread of contagion on networks through the following focus areas:

• Dynamical models: Developing realistic models for the spread of diseases and ideology on networks, particularly higher-order networks

• Bayesian Inference: Reconstructing networks and disease dynamics from imperfect and noisy observational data

• Higher-order network structure: Developing higher-order measures sensible for the spread of diseases and information

• Software development: Developing scientific Python software to support research and facilitate the dissemination of results

The candidate will also have considerable freedom to tackle any related topics of interest.