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 to understand the genetic and molecular basis of gene regulation, characterize the role of gene regulatory factors in determining cell identity and function, and uncover the gene regulatory basis of human diseases.
Research areas that we are excited about include, but are not limited to, development of new statistical and machine learning methods for analysis of high-dimensional multi-modal omics data, machine learning-based sequence-to-activity models, and AI models for omics-based diagnosis and prognosis of diseases.
As a postdoctoral researcher, you will develop and lead a world-class project to address biological questions at the forefront of genomics and gene regulation. As part of this project, you will develop methods based on machine learning and/or statistical inference to analyze various omics data, characterize cellular phenotypes and trajectories, understand how cell identity and behaviour is encoded in the genomic sequence, and/or uncover the gene regulatory basis for development and progression of human diseases such as cancer.
The CSG Lab is part of the Victor Phillip Dahdaleh Institute of Genomic Medicine, located at the heart of McGill University. The Institute provides cutting-edge research environment and facilities for genomics, epigenomics, and computational biology, and hosts more than 200 faculty, students, and staff. In addition to state-of-the-art research environment and a highly competitive salary, this postdoctoral position provides extensive opportunities for interdisciplinary collaborations with world-leading researchers within and outside McGill University.
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.
We are interested in a broad range of backgrounds related to computational biology and genomics. The ideal candidate will have strong analytical and programming skills, demonstrated expertise in development or application of methods based on machine learning and/or statistical inference, and familiarity with methods for analysis and modeling of large-scale genomics data, such as single-cell omics, short- and/or long-read RNA-seq, and/or ChIP-seq.
Interested applicants should send a cover letter, CV, and the contact information of at least two references to hamed.najafabadi@mcgill.ca.