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Postdoctoral Researcher

Job in Toronto, Ontario, M5A, Canada
Listing for: University Health Network
Full Time, Seasonal/Temporary, Contract position
Listed on 2026-02-18
Job specializations:
  • Research/Development
    Clinical Research, Research Scientist, Data Scientist, Medical Science
Salary/Wage Range or Industry Benchmark: 60000 - 70000 CAD Yearly CAD 60000.00 70000.00 YEAR
Job Description & How to Apply Below

Company Description

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care.

UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

Job Description

Union: Non-Union
Number of Vacancies: 1
New or Replacement Position: New
Site: Princess Margaret Cancer Centre, MaRS Tower
Department: Princess Margaret Research Institute
Reports to: Principal Investigator
Hours: Hours Per Week
Shifts: Monday to Friday
Salary: $60,000 - $70,000
Status: Temporary Full-time
Closing Date:
February 20, 2026

Position Summary:
A computational postdoctoral researcher position is available in the laboratory of Dr. Gregory Schwartz to study cellular diversity in response to anti-cancer therapies. The Schwartz Lab is located within the heart of the Discovery District, at the intersection of the Princess Margaret Cancer Centre, the University of Toronto, and MaRS, the centre of a rich network of scientists and clinicians.

The Schwartz Lab studies the contribution of cellular heterogeneity to therapeutic response and cancer evolution. Towards this goal, the laboratory develops novel multi-omic and single-cell computational methods to improve cancer diagnosis and treatment through precision medicine. Notably, work from researchers in the laboratory include new methods for inferring cell-cell communication at the interface of tumour and stroma, integrating transcriptomics and proteomics data to identify novel biomarkers across cancer subtypes, and clustering and visualization algorithms for single-cell transcriptomics, epigenomics, and spatial multiomics.

For more information on the types of tools we will develop, please visit .

This position is a temporary full-time postdoctoral appointment for an initial one-year term, with the possibility of renewal for additional years, subject to performance, funding availability, and program requirements.

Duties:

  • Work with data from cutting-edge technologies (single-cell transcriptomics, single-cell epigenomics, spatial transcriptomics, spatial multiomics)
  • Develop novel computational methods to identify cellular populations using multiomic integration
  • Track cancer-cell evolution across treatments using deep-learning architectures
  • Identify biomarkers associated with different anti-cancer treatments
  • Determine tumor microenvironmental factors associated with disease progression and treatment resistance
  • Disseminate research through national / international conferences and high-impact publications
  • Develop reproducible pipelines for high-throughput data analysis
  • Enhance writing skills by contributing to grant applications
  • Qualifications

  • Awarded a PhD within the previous 5 years or graduating candidate with a degree in Computer Science, Mathematics, Bioinformatics, or Computational Biology, or related field.
  • Proven track record of published method development
  • Strong background in Linux and Python
  • Strong background in statistics, machine learning, and/or deep learning
  • Excellent writing and verbal communication skills
  • Experience with version control, specifically Git and Git Hub
  • Experience with Next generation sequencing data formats such as FASTQ, BAM, Matrix Market, and HDF5 is a plus
  • Experience with Haskell, R, and LaTeX is a plus
  • Willingness to work in a team environment as well as independently
  • Additional Information

    Why join UHN?

    In addition to working…

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