Genomic Surveillance Meets Machine Learning: Predicting the Origins of Salmonella Outbreaks with Machine Learning

Project Code

MRCPHS24Br Reyher

Research Theme

Population Health Sciences

Full Project Description

Please download the below document for a full project description and to see the full supervisory team.


Gastrointestinal disease caused by the consumption of contaminated food is a major public health concern, requiring significant resources to identify the source of infections. The successful candidate will work with the UK Health Security Agency to develop cutting-edge genomic machine learning models to predict the source of foodborne pathogens. These tools will then be used to support public health decision making and facilitate rapid responses to future outbreaks.

Lead Supervisor

Professor Kristen Reyher

Lead Supervisor Email

University Affiliation