609 days ago
Institution/Company:

University Health Network / The Princess Margaret Cancer Centre

Location:

Toronto

, Ontario

 Canada

Job Type:

Postdoctoral

Degree Level Required:

PhD, Postdoctoral

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Description:

A computational postdoctoral position is available in the laboratory of Dr. Gregory Schwartz to study the role of cellular heterogeneity in cancer. The laboratory 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 center of a rich network of scientists and clinicians.

The laboratory 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 integration of transcriptomics and proteomics data to identify novel biomarkers across cancer subtypes, mutation detection tools characterizing new classes of internal tandem duplications, and clustering and visualization algorithms for single-cell transcriptomics and epigenomics. For more information on the types of tools we will develop, please visit https://schwartzlab-methods.github.io/.

Responsibilities:

By joining the laboratory, the successful candidate will have the opportunity to:

  • Work with data from cutting-edge technologies (single-cell transcriptomics, single-cell epigenomics, spatial transcriptomics, spatial proteomics)

  • Develop novel computational methods to identify cellular populations using multi-omic integration

  • Predict cell-cell communication at the resolution of individual cells.

  • Track cancer-cell evolution across treatments

  • 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 publications

  • Develop reproducible pipelines for high-throughput data analysis

Qualifications:
  • Ph.D. received within the last 5 years or graduating Ph.D. candidate with a degree in Computer Science, Bioinformatics, or Computational Biology, or related field.

Additional Information:

Transforming lives and communities through excellence in care, discovery and learning.

The University Health Network, where “above all else the needs of patients come first”, encompasses Toronto Rehabilitation Institute, Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre and the Michener Institute of Education at UHN. The breadth of research, the complexity of the cases treated, and the magnitude of its educational enterprise has made UHN a national and international resource for patient care, research and education. With a long tradition of groundbreaking firsts and a purpose of “Transforming lives and communities through excellence in care, discovery and learning”, the University Health Network (UHN), Canada’s largest research teaching hospital, brings together over 16,000 employees, more than 1,200 physicians, 8,000+ students, and many volunteers. UHN is a caring, creative place where amazing people are amazing the world.

University Health Network (UHN) is a research hospital affiliated with the University of Toronto and a member of the Toronto Academic Health Science Network. The scope of research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. Research across UHN’s five research institutes spans the full spectrum of diseases and disciplines, including cancer, cardiovascular sciences, transplantation, neural and sensory sciences, musculoskeletal health, rehabilitation sciences, and community and population health. Find out about our purpose, values and principles here.

Keywords:

bioinformatics

computational biology

machine learning

algorithms

multi-omics

big data

cancer

immunology

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