Postdoctoral Researcher

Union: Non-Union
Number of Vacancies: 1
New or Replacement Position: New
Site: MaRS
Department: Research
Reports to: Senior Scientist
Salary Range: $54,902 - $82,352 annually
Hours: 37.5 Hours Per Week
Shifts: Monday - Friday; Day Shifts
Status: Temporary Full-time (2-Year Contract)
Closing Date: November 26, 2026
Position Summary
We seek a postdoctoral fellow with strong expertise in computational biology, bioinformatics, biomedical data science, and/or AI/ML to join a major interdisciplinary initiative focused on developing computational approaches for translational cancer research.
The ideal candidate will have a strong quantitative or computational background together with demonstrated experience in AI applied to genomic, transcriptomic, multi-omics, molecular, pharmacological, or clinical data. The position is particularly suited to candidates interested in addressing biologically and clinically meaningful questions at the intersection of computational oncology, cancer genomics, machine learning, biomarker discovery, and scientific AI.
A major focus of the position will be computational biomarker discovery and therapeutic response modeling across cancer types and treatment modalities. This will include identifying predictive and response-associated molecular signatures, characterizing disease- and treatment-related biological mechanisms, prioritizing potential therapeutic targets, and developing computational models of drug and treatment response. Methodological priorities will include multimodal and representation learning, as well as foundation-model approaches that integrate multiple biological and clinical data layers to improve prediction, generalization, and biological interpretation.
A second major focus will be the development of scalable, reproducible, and privacy-aware computational frameworks for multi-cohort translational studies. This will include agentic AI-enabled approaches for data harmonization, annotation, quality control, integration, analysis, and scientific interpretation across public and institutional datasets, as well as distributed and federated approaches in settings where sensitive data remain under local governance.
Together, these efforts aim to advance computational oncology, biomarker development, therapeutic discovery, and precision medicine by combining modern machine learning with rigorous computational biology, multimodal biomedical data integration, and reproducible distributed analysis.
The candidate will be working in the Haibe-Kains Lab at the Princess Margaret Cancer Centre, University Health Network, within a multi-institutional research collaboration involving the Rosalind & Morris Goodman Cancer Institute at McGill University, as well as other academic, clinical, and industry partners.
What We Offer
A highly collaborative, interdisciplinary research environment spanning computational biology, oncology, clinical informatics, and AI
Access to large-scale proprietary clinical datasets (tumour biopsies, treatment outcomes, longitudinal genomics) alongside major public resources
State-of-the-art GPU cluster and cloud computing infrastructure with dedicated allocations
Mentorship from senior faculty with strong track records in Nature/Science-family publications and competitive grant funding
Opportunities to collaborate with clinical teams, pharmaceutical industry partners, and international research consortia
Support for conference travel, professional development, and independent fellowship applications
Duties
Design and implement multimodal computational and foundation models integrating molecular, genomic, transcriptomic, multi-omics, treatment, and clinical data.
Develop transfer learning strategies to therapeutic prediction tasks, including drug response, mechanism of action, synergy, and repurposing.
Build secure, scalable, and privacy-preserving AI/ML workflows for biomarker discovery and translational research, aligned with responsible data sharing, governance, ethics, and regulatory requirements.
Build flexible AI/ML workflows that reduce reliance on task-specific architectures and enable generalization across therapeutic contexts, tumour subtypes, and treatment modalities.
Architect and deploy secure, scalable, and privacy-preserving computational infrastructure for biomarker discovery and translational cancer research.
Develop agentic AI-enabled frameworks to support the harmonization, annotation, quality control, and integration of clinical, genomic, and transcriptomic data across public cohorts and private institutional datasets.
Implement distributed and federated analysis pipelines in which each contributing dataset can be analysed separately, enabling multi-cohort biomarker assessment without raw data centralization.
Develop systematic workflows to evaluate published and user-specified DNA and RNA signatures, including immune, stromal, mutation-based, pathway-level, and treatment-response signatures.
Assess the predictive value of molecular signatures across cancer types and treatment modalities, including chemotherapy, targeted therapy, immunotherapy, and emerging therapeutic approaches.
Integrate biomarker discovery and immunotherapy inference pipelines with clinical data warehouses to support translational studies in collaboration with clinical, industry, and computational partners.
Contribute to responsible data sharing frameworks, data governance processes, IRB/ethics protocols, and regulatory documentation as required.
Collaborate closely with computational biologists, software developers, clinicians, and wet-lab scientists to build, validate, and translate predictive models and biomarker discovery workflows.
PhD completed, or expected within 3 months of submission, in a relevant quantitative or biomedical discipline, including but not limited to:
Computational Biology, Bioinformatics, Systems Biology, and/or Quantitative Genomics
Machine Learning, Artificial Intelligence, Computer Science, and/or Data Science
Biostatistics, Biomedical Engineering, or related fields
Demonstrated experience developing or applying computational, statistical, machine-learning, or deep-learning methods to molecular, biological, clinical, or multi-omics data.
Expertise in one or more modern AI/ML approaches relevant to foundation models or representation learning, such as graph neural networks, transformers, generative models, self-supervised learning, or transfer learning.
Strong programming skills in Python and/or R, with practical experience using modern machine learning frameworks and tooling such as PyTorch, Hugging Face, PyTorch Geometric, Deep Graph Library, scikit-learn, or equivalent platforms.
Experience working with large-scale biomedical datasets, such as molecular graphs, transcriptomic perturbation profiles, multi-omics data, clinical genomics, electronic health record-derived data, or treatment-response datasets.
Familiarity with agentic AI, tool-using AI systems, data harmonization, federated learning, privacy-preserving analysis, or secure distributed computing is highly desirable.
Proficiency with reproducible workflow management systems such as Snakemake, Nextflow, CWL, or equivalent pipeline frameworks.
Familiarity with cloud or high-performance computing environments, such as GCP, AWS, SLURM-based clusters, or equivalent infrastructure.
Understanding of data harmonization, privacy-preserving analysis, federated learning, secure distributed computing, or clinical data governance is highly desirable.
Strong publication record, commensurate with career stage, in computational biology, AI/ML, bioinformatics, biostatistics, biomedical data science, or related fields.
Excellent communication skills and ability to work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research.
Experience with clinical data harmonisation (e.g., OMOP CDM, HL7 FHIR) is an advantage.
Experience designing scalable bioinformatics pipelines for large-scale genomic or transcriptomic datasets.
Understanding of regulatory and data governance requirements in clinical research settings.
Prior collaborative work across computational and clinical or wet-lab research teams.

Employment type
Full-time

Work arrangement
No