Research interests/description of main research theme:
In order to better guard against the rapid spread of high-risk strains of infectious diseases in the future, we must better leverage the large scale collection and processing of genomic data that is possible with today’s technologies. A number of international initiatives are currently exploring the implementation of genomic surveillance to track the prevalence of existing pathogens and identify emerging pathogens. These programs will produce enormous amounts of data which must be processed quickly to discover concerning trends.
This project aims to develop computational approaches to better link genotype to phenotype and detect concerning patterns in pathogen spread and evolution over time and space.
Applicants should have a background in bioinformatics, biology, biochemistry, physics, mathematics, computer science, or a related subject. The project will involve computational analysis of DNA sequence data and require programming skills. Prior proficiency in Python is preferred. An ideal candidate will have experience analyzing microbial genomic data or working with kmer and/or graph-structured data.
Applicants should hold or realistically expect to obtain at least an Upper Second Class Honours Degree in a relevant subject (see above).
How to apply
Informal enquiries should be directed to Nicole Wheeler
Applications should be directed to Dr Nicole Wheeler ([Email Address Removed]). To apply, please send:
• A detailed CV, including your nationality and country of birth;
• Names and addresses of two referees;
• A covering letter highlighting your research experience/capabilities;
• Copies of your degree certificates with transcripts;
• Evidence of your proficiency in the English language, if applicable.