Postdoctoral Fellow in Computational and Statistical Genomics

McGill University
Montreal, QC, Canada
Job Type:
  • Postdoctoral
Degree Level Required:
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Postdoctoral Fellow in Computational and Statistical Genomics

McGill University invites applications for a post-doctoral position in the Computational and Statistical Genomics lab ( at McGill University and Génome Québec Innovation Centre. 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

  • Regulatory programs that govern mRNA stability and decay: We are working on novel computational approaches based on Bayesian inference and machine learning to measure the decay rate of individual mRNAs from RNA-seq data, identify factors such as RNA-binding proteins and microRNAs that regulate the mRNA decay, and reveal their role in development of human diseases. Particularly, we are interested in the role of RNA stability in cancer and neurodegenerative diseases. More information can be found in the following publications:

  • Single-cell maps of gene regulatory programs: We are developing new algorithms based on probabilistic modeling for analysis of single-cell RNA-seq and ChIP-seq data, in order to measure the abundance, transcription rate, and decay rate of individual mRNAs at the single-cell level, identify regulatory factors such as transcription factors, RNA-binding proteins and microRNAs that contribute to the cellular heterogeneity, and explore their role in tumour development.

  • Mechanisms of DNA binding and gene regulation by zinc finger transcription factors: The human genome encodes >1700 transcription factors, about half of which belong to the C2H2 zinc finger family. We are working on innovative methods based on machine learning, protein structure modeling, and functional genomics to characterize the mechanisms that underlie the interaction of these proteins with the DNA and the exceptional diversity of their regulatory functions. We are particularly interested in understanding how the zinc finger transcription factors read and interpret the epigenetic modifications of DNA. Representative publications include:




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, 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.

  • How to Apply

    Interested applicants should send a cover letter, CV, and the contact information of at least three references to

Additional Information

McGill University and Génome Québec Innovation Centre is a world-class genome center located at the heart of McGill University. The Centre provides cutting-edge research environment for genomics, epigenomics, and computational biology, and hosts more than 200 faculty, students, and staff. The Centre is equipped with state-of-the-art genomics and computational facilities, including the capacity to generate, store and analyze more than 800 Tbp of sequencing data per year. McGill University is committed to equity in employment and diversity. It welcomes applications from indigenous peoples, visible minorities, ethnic minorities, persons with disabilities, women, persons of minority sexual orientations and gender identities, and others who may contribute to further diversification.