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. Bioinformatic analysis and novel integrative approaches of multi-omic data sets, including RNA-seq, proteomics, whole-exome sequencing, ATAC-seq, and Cut’n’Run data sets, will be required. The candidate will be co-supervised by Drs. Laurie Ailles and Benjamin Haibe-Kains.
- Doctorate in computational biology, computer science, statistics, or applied mathematics.
- Published/submitted papers in cancer genomics and/or machine learning research.
- Experience with analysis of high-throughput omics data, such as next-generation sequencing and gene expression microarrays, in cancer research.
- Very strong expertise in programming and machine learning (R, C/C++, Python and Unix programming environments).
- 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).
- Some background in biology/wet-lab research would be a plus.
Position status: Temporary Full-time
Closing Date: Until filled
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