Postdoctoral Fellowship in Computational Cancer Biology

Princess Margaret Cancer Centre
Toronto, ON, Canada
Job Type:
  • Postdoctoral
Degree Level Required:
Apply Now

Postdoctoral Fellowship in Computational Cancer Biology

The Ailles Lab uses primary tumor tissues and patient-derived models of cancer to understand cancer biology and develop personalized approaches to cancer therapy. The Haibe-Kains Lab focuses on developing novel machine learning approaches for biomarker discovery from large pharmacogenomic data. We seek a postdoctoral fellow to participate in multiple projects to identify novel prognostic and predictive biomarkers, interactions between cancer cells and their microenvironment, and identification of novel therapeutic targets.

Dr. Ailles’ lab will host the candidate. Dr. Ailles has over 15 years of experience in stem cell and cancer biology. Areas of research include cancer stem cells, cancer-associated fibroblasts, clonal heterogeneity and epigenetics. Our research utilizes primary patient-derived cancer tissue specimens, as well as patient-derived primary cultures and xenografts. Diseases studied include head and neck squamous cell carcinoma, high-grade serous ovarian cancer and clear cell renal cell carcinoma. We have established a “living biobank” of patient-derived xenografts that can be used to assay cancer stem cells, evaluate drug responses and development of drug resistance, and to perform a wide range of novel, clinically relevant studies. We collaborate extensively with other labs, clinicians and clinician scientists. Future studies will include extensive genomic and proteomic profiling of patient tissues and patient-derived model systems.

The candidate will be co-supervised by Dr. Haibe-Kains, who has over 10 years of experience in computational analysis of genomic and transcriptomic data, in the context of translational research. He is the (co-)author of more than 150 peer-reviewed articles in top bioinformatics and clinical journals.


Bioinformatic analysis and novel integrative approaches of multi-omic data sets, including RNA-seq, proteomics, whole-exome sequencing, ATAC-seq, and Cut’n’Tag data sets; • Development of innovative computational methods and open-source bioinformatics software; • Visualization and interpretation of –omics data with pathway and network information; • Writing peer reviewed papers and grant proposals.


Hands-on experience in high performance computing, especially for parallelizing code in C/C++ (openMP) and/or R in a cluster environment (e.g., Sun Grid Engine/Torque); • Strong scientific publication record in peer-reviewed journals on bioinformatics and/or cancer research; • Strong understanding and interest in molecular biology, genomics, and cancer; • Ability to work independently as well as part of a fast-paced and collaborative team; • Excellent verbal and written communication skills in English; • Some background in biology/wet-lab research would be a plus

  • How to Apply

    Submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references to All documents should be provided in PDF.