Bioinformatics Analyst Position in Cancer Genomics

[sushant kumar]

Bioinformatics Analyst

The Kumar Lab at the Princess Margaret Cancer Centre and The University of Toronto is looking for an enthusiastic and highly motivated Bioinformatics Analyst. This position is ideal for recent graduates and computational scientists seeking opportunities to work in cancer genomics. Our research interests include developing and applying novel computational frameworks that leverage large-scale multi-omics and biomolecular structural data to identify clinically actionable cancer biomarkers and study tumor evolution. Bioinformatics analysts will work closely with graduate students, post-doctoral associates, clinicians, and experimental collaborators to develop new integrative methods and software to identify clinically actionable cancer biomarkers.

Responsibilities:

• Analyze large-scale biomolecular and sequencing data from major internal and external resources 

• Develop novel statistical and machine learning-based methods and tools 

• Collaborate with members in the lab and beyond to analyze and interpret biomolecular data, including sequencing, functional genomics, clinical, and biomolecular simulation data 

• Contribute to the development and testing of data science workflows and implementations in high-performance and cloud computing environments 

• Contribute toward maintenance of lab code base and software packages 

• Document and present results in written and oral reports to other lab members

Qualifications:

• At a minimum, a bachelor’s degree in bioinformatics, computer science, or statistics/data science 

• Minimum one (1) year of practical and related experience 

• Proficiency in programming (e.g., R, Python, BASH, PyTorch/TensorFlow) 

• Experience working in a Unix-based environment 

• Prior experience in next-generation sequence data analysis and other biological data analysis is preferred 

• Team-oriented with excellent written and verbal communication skills 

• Experience with the application of machine learning/deep learning methods for biological data is preferred 

• Prior experience in working in HPC and cloud computing environments is preferred 

• Knowledge of scientific workflow development (e.g., Snakemake, Nextflow, CWL, etc.) and experience with the use of containers (e.g., Docker, Singularity, etc.) is preferred

Application process:

To apply for this position, please send a) cove letter, b) curriculum vitae, and c) name of two references to ccgl...@gmail.com.