Open postdoctoral positions

Understanding the genetic basis of COPD through functional genomics.

The research group of Dr. Maxwell Libbrecht in Simon Fraser University's department of Computing Science seeks a postdoctoral researcher to work on a project that aims to develop statistical and machine learning methods to understand the genetic basis of COPD. Chronic obstructive pulmonary disease (COPD) is a progressive, inflammatory lung disease that affects more than 300 million people and is the 3rd leading cause of death worldwide. This project aims to leverage data from genome-wide association studies (GWAS) and functional genomics data from the ENCODE consortium to improve understanding of COPD. The project is in collaboration with Dr. Ma'en Obeidat and the Providence Health Care Providence Airway Centre.

The successful applicant will develop and apply machine learning and probabilistic graphical model methods to integrate GWAS, functional genomics and other data sets. Applicants should have a PhD in computer science, statistics, molecular biology, bioinformatics or a related field. Experience with genomic sequencing and/or probabilistic graphical models is desired. Candidates should have strong communication skills in English.

Interested applicants should send a CV, a cover letter, and two relevant peer-reviewed publications to maxwl@sfu.ca, with "Postdoctoral fellowship - COPD" in the subject line.

Predicting drug resistance in pathogenic bacteria through deep learning.

Pathogenic microbial organisms cause a significant burden of disease, particularly due to the problem of drug resistance, whereby a pathogen no longer responds to treatment by one or more available drugs. 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 machine learning and algorithmic techniques for microbial WGS data. Machine learning (in particular, deep learning) provides a tool to predict and understand the relationship between drug resistance and genotype.

The successful applicant will develop and apply deep learning methods for this task. The project will involve developing new neural network strategies and architectures and applying these methods to improve understanding and treatment of drug resistance in bacterial pathogens. The ideal candidate will have a PhD in computational biology, computer science, 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 machine learning, including deep learning, is an asset. The candidate must have strong written and verbal skills in English. The candidate will be based at Simon Fraser University's School of Computing Science, with possibility of a secondary appointment at the British Columbia Centre for Disease Control (BC CDC). The position will be jointly supervised by Leonid Chindelevitch and Maxwell Libbrecht (Simon Fraser University, School of Computing Science) and funded by a Bioinformatics/Computational Biology grant from Genome Canada.

Interested applicants should send a CV, a cover letter, and two relevant peer-reviewed publications to leonid@sfu.ca and maxwl@sfu.ca, with "Postdoctoral fellowship - DR/ML" in the subject line. More information is available here.