We invite applications for a postdoctoral position in the CSG lab ( csg.lab.mcgill.ca ) at McGill University. The successful candidate will join an interdisciplinary team of computational and experimental biologists working at the intersection of machine learning, statistical inference, and genomics, in order to understand the genetic and molecular basis of gene regulation, the role of gene regulation in cancer, and gene regulatory mechanisms that can be exploited to inhibit the cancer cell.
Main areas of research:
We are broadly interested in understanding the regulatory programs that drive cancer cell function and behavior. Some of the current areas of focus are summarized here:
- Single-cell maps of gene regulatory programs in cancer: We are developing new algorithms based on probabilistic modeling for analysis of single-cell transcriptomics data, in order to measure the abundance, transcription rate, and decay rate of individual mRNAs at the single-cell level and identify regulatory networks that contribute to the cellular heterogeneity. By combining these algorithms with single-cell transcriptome data that we are generating from primary and metastatic tumors, we aim to understand the gene regulatory basis of tumorigenesis and metastasis. Relevant publications include:
www.biorxiv.org/content/10.1101/617225v5
www.cell.com/cell-reports/fulltext/S2211-1247(18)30575-8
- Mechanisms that govern post-transcriptional regulation of mRNAs: We are working on novel computational approaches based on Bayesian inference and machine learning to measure the dynamics of mRNA processing and decay from RNA-seq data, identify factors such as RNA-binding proteins and microRNAs that regulate these processes, and reveal their role in development of cancer. More information can be found in the following publications:
www.nature.com/articles/s41467-017-00867-z
www.cell.com/cell-reports/fulltext/S2211-1247(18)30575-8
www.nature.com/articles/nature11013
- The regulatory consequences of somatic mutations in transcription factors: We are developing new methods based on machine learning, protein structure modeling, and functional genomics to characterize the mechanisms that underlie the interaction of transcription factors with DNA, how they read and interpret the epigenetic modifications of DNA, and how their mutations affect their function and contribute to the transcriptome remodeling in cancer. Representative publications include:
www.nature.com/articles/nbt.3128
www.biorxiv.org/content/10.1101/630756v3
While we are interested in a broad range of backgrounds related to computational genomics, candidates with a PhD in bioinformatics, computational biology or related areas are particularly encouraged to apply. Strong analytical and programming skills, as well as experience with bioinformatics tools and data resources are desirable. Preference will be given to candidates who have experience in design, implementation, or application of computational methods for analysis of large-scale genomics data, such as ChIP-seq, RNA-seq, scRNA-seq, and WGS. Familiarity with methods in machine learning and statistical inference is a plus.
Successful candidates should have the ability to work both independently and as a team member in a multi-disciplinary environment.
Interested applicants should send a cover letter, CV, and the contact information of at least two references to hamed.najafabadi@mcgill.ca.