Genomic surveillance of infectious diseases has been a revolutionary public health success and a critical tool in elucidating pathogen transmission, evolution and source tracking. However, exploiting this data to understand the genomic drivers of pathogen success and virulence and using this data predictively remains underdeveloped. Your job
We have recently received funding to leverage advances in pan-genomics and deep-learning to build predictive models to help understand why certain microbial variants are successful pathogens and what emerging variants to prioritise.
The focus of this research is to be applied to the realms of pandemic preparedness and antimicrobial resistance. Specifically, we aim to build genomic driven classifiers that can identify what genomic changes are predictive for pandemic spread in terms of both the micro-organism and their resistance cargo. There is a critical need to better predict the differential risk that different strain types pose such that proportional interventions that maximise public safety and minimise economic losses are developed.
The project is a collaboration between Utrecht University and national and international partners and you will be a key member of the project team. You will be responsible for:
- phylogenetic and phylodynamic and pan-genomic analysis;
- design and validation of machine learning models for the prediction of spread of antimicrobial resistance.