- Simon Fraser University/University of British Columbia
- Vancouver, BC, Canada
- Job Type:
- Degree Level Required:
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Pathogenic microbial organisms cause a significant burden of disease not only in low-resource countries, but also in high-income countries, especially in hospital settings. One challenge that is particularly relevant today is the problem of drug resistance, whereby a pathogen no longer responds to treatment by one or more available drugs. Additionally, the frequency of pathogen outbreaks requires the development of surveillance tools to rapidly track, prevent, and ultimately disrupt the chain of transmissions.
The availability of fast, reliable and affordable whole-genome sequencing (WGS) methods has the potential to be a major boon for public health authorities attempting to control the development of drug resistance and the spread of epidemic outbreaks. However, in order to fully harness the power of these methods there is an urgent need for novel statistical and algorithmic techniques for microbial WGS data. These methods are still in their infancy, and developing them further could lead to a significant impact.
The successful applicant for this position will extend state-of-the-art computational statistics methodology to address key challenges from fields such as pathogen genome-wide association studies ; phylodynamics , for example methodological developments related to Approximate Bayesian Computation (ABC) or Sequential Monte Carlo (SMC) ; and molecular epidemiology . The specific focus will depend on the background of the successful candidate.
The ideal candidate will have a PhD in computational biology, statistics, applied mathematics, or a related field. The candidate should be a self-starter, able to work independently and assist in supervising MSc or PhD students. Experience working with genomic data, and a knowledge of phylodynamics, GWAS, ABC and/or SMC methodologies is an asset.
How to Apply
Please send a CV and one or two relevant peer-reviewed publications