Degree Level Required:
Developing effective targeted and rational therapies against cancer cells is a challenging task given the substantial variations in molecular and cellular landscapes between and within tumors. Single-cell profiling approaches have advanced our understanding of the extent of cellular diversity in tumor ecosystem, and its role in immune evasion and tumor progression. A full-resolution understanding of these interactions and functional inter-dependencies between tumor cells and stromal constituents in tumor microenvironment (TME) will help uncover mechanisms that underlie immune evasion and therapy resistance in tumors, and will open new avenues for therapeutic innovations. Toward this goal, we dissect functional heterogeneity among cancer and stromal cells by investigating diversity in active gene expression patterns using single-cell RNA-seq (scRNA-seq). We also leverage the power of in situ spatial transcriptome and proteome profiling to elucidate cellular organization within tissues. This is critical for understanding the complex interplay between diverse cell types within tumors. By integrating the scRNA-seq data with spatial transcriptome profiles we aim to identify potential interactions and cellular inter-dependencies that fuel tumor progression and therapy resistance.
This research program is built on joint-forces from expert clinicians and scientists at McGill University Health Centre (MUHC), McGill Genome Centre (MGC) and Goodman Cancer Institute (GCI) to create a trans-disciplinary research program that leverages the expertise in genomics, computational science and statistics, cancer biology, histopathology, and cutting-edge tumor models to address the aforementioned unmet needs in order to improve patient survival and disease outcomes.
The successful candidate will join an interdisciplinary team of experimental and computational biologists working to understand the genetic and epigenetic basis of cancer development and progression, and to develop innovative therapeutic approaches toward Precision Medicine for cancer. The main focus of this position will be to establish and develop innovative computational approaches for analyzing large-scale (e.g. whole-transcriptome) spatial genomics/transcriptome as well as scRNA-seq data, and integrating these datasets toward a deep-resolution understanding of cellular communications within the tumor microenvironment. The work involves substantial collaboration with experimental and clinical researchers who generate these large-scale data and provide access to clinical information of samples.
While a broad range of backgrounds related to cancer cell biology and genomics are suitable, 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 for scRNA-seq analysis 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 scRNA-seq or spatial profiling data. Successful candidates should have the ability to work both independently and as a team member in a multi-disciplinary environment. Experience with applying machine-learning approaches to genomics data is a plus.
We are inviting applications from postdoctoral candidates to join this world-class program. We are committed to equity in employment and diversity, and welcome 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.