Cancer Genomics Postdoctoral Associate

Successful candidates will be recent PhD graduates who are interested in making fundamental discoveries in tumor evolution and immune response that can lead to translational impact in aggressive and largely incurable cancers.

Research approaches in our team include computational analyses of tumor multi-omic profiles at bulk and single cell level, with spatial and temporal resolution within patient samples. We are interested in how tumor cells cooperate with non-neoplastic cells of the microenvironment to respond to selective pressures in general, and in particular to standard therapy regimes. The focus of the candidate will be on establishing methods to integrate long-read sequencing data into our current workflows for genome and transcriptome analyses. Key goals include identification of novel biomarkers and targets for therapy that can be further pursued within a fully operational preclinical pipeline toward clinical translation.

The lab is part of a multi-disciplinary team of cancer researchers filled with passionate and determined individuals who will provide the successful candidate with a stimulating and conducive environment to participate and grow as a scientist. The research is conducted at the University of Calgary, in close collaboration with local and international scientists and clinicians, and thus will also provide the successful candidate with opportunities to learn from a large network of talented professionals.

The PDF will lead analyses of genome and transcriptome data from patient samples, profiled at bulk or single single cell resolution, and with a focus on long-read technologies. The candidate will use statistical and machine learning approaches to interpret data in the context of tumor evolution, with the aim of (1) understanding the dynamics of tumor growth, relapse, metastasis and progression, and (2) defining novel biomarkers of progression and targets for rational therapy. The PDF will design and carry out computational experiments and lead the writing of scientific manuscripts.

• PhD with training in bioinformatics, computer science, statistics or cell biology
• Experience working in a Unix and HPC environment (shell scripting, command-line tools)
• Experience conducting large-scale data analyses (genomic/multi-omic)
• Expertise with machine-learning methods, and statistical analyses using R or python
• Experience with assembly methods is highly desirable
• Experience with long-read technologies is highly desirable
• Understanding of cancer cell biology or immunology is highly desirable
• Evidence of scientific accomplishment via peer-reviewed publications is required
• Highly motivated with excellent organizational, problem-solving and communication skills (verbal and written)