Computational and Statistical Genomics

The Computational and Statistical Genomics (CSG) lab at McGill University is recruiting a Postdoctoral Researcher. 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 the gene regulatory mechanisms that can be exploited to inhibit the cancer cell.

Main areas of research

  • Single-cell maps of gene regulatory programs: We are developing new algorithms for analysis of single-cell RNA-seq data in order to measure the transcription and decay rates of individual mRNAs at the single-cell level, identify transcriptional and post-transcriptional regulatory factors that drive cellular reprogramming and heterogeneity, and explore their role in tumour development. Our research spans a wide range of problems from optimal preprocessing of single-cell data to reconstruction of gene regulatory networks using the scRNA-seq datasets that we are generating for different cancer models. For example, see Farouni et al. 2019.
  • Regulatory programs that govern RNA splicing and decay: We are working on novel computational approaches based on Bayesian inference and machine learning to measure the splicing and decay rate of individual mRNAs from RNA-seq data, identify factors such as RNA-binding proteins and microRNAs that regulate these processes, and reveal their role in development of human diseases. Particularly, we are interested in the role of RNA stability in cancer and neurodegenerative diseases. Example publications include Alkallas et al. 2017 and Perron et al. 2018. 
  • Mechanisms of DNA binding and gene regulation by transcription factors: The human genome encodes >1600 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 molecular mechanisms that underlie the interaction of these proteins with the DNA, their functions in the cell, and their role in cancer. Representative publications include Dogan et al. 2019Najafabadi et al. 2015, and Najafabadi et al. 2017.

Qualifying skills and abilities

We are interested in a broad range of backgrounds related to computational and functional 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 single-cell RNA-seq. Familiarity with methods in machine learning and statistical inference is a plus.

How to apply

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

Hamed S. Najafabadi, Ph.D.

Assistant Professor, Department of Human Genetics, McGill University
McGill University and Génome Québec Innovation Centre

This position is available immediately. Applications will be considered until the position is filled.


About McGill University and Génome Québec Innovation Centre

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 3,000 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.