University Health Network
Postdoctoral Researcher
Toronto, ON, Canada · Contract
Sponsorship not specified$55k-$93kDetected 2 days ago
PythonHaskellAlgorithmsGitLinuxMachine LearningDeep LearningData AnalysisStatisticsBioinformaticsNGSLaTeXResearchCommunicationWriting
About the role
- 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.
Responsibilities
- Develop novel computational methods to identify cellular populations using multiomic integration
- Develop reproducible pipelines for high-throughput data analysis
- Towards this goal, the laboratory develops novel multi-omic and single-cell computational methods to improve cancer diagnosis and treatment through precision medicine.
- For more information on the types of tools we will develop, please visit https://schwartzlab.ca/.
Requirements
- Awarded a PhD within the previous 5 years, with a degree in Computer Science, Mathematics, Bioinformatics, or Computational Biology, or related field.
- Experience with version control, specifically Git and GitHub
Nice to have
- 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
Skills
- Enhance writing skills by contributing to grant applications
- Excellent writing and verbal communication skills
Compensation
- $54,902 - $93,333 Annually
Benefits
- 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'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.
- Track cancer-cell evolution across treatments using deep-learning architectures
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