Postdoctoral Researcher

Responsibilities

• Design and implement multimodal drug foundation models integrating molecular graphs and multi-omics data
• Develop transfer learning strategies for drug prediction tasks including mechanism of action and toxicity profiling
• Architect secure, scalable, and privacy-preserving computational infrastructure for biomarker discovery
• Develop agentic AI-enabled frameworks for data harmonization across clinical and genomic datasets
• Implement distributed and federated analysis pipelines for multi-cohort biomarker assessment
• Integrate biomarker discovery pipelines with clinical data warehouses
• Collaborate with computational biologists, clinicians, and wet-lab scientists

Requirements

• PhD awarded within the last 5 years (or MD/DDS within 10 years) in a quantitative or biomedical discipline
• Experience developing deep learning methods for molecular or multi-omics data
• Expertise in AI/ML approaches such as GNNs, transformers, or generative models
• Strong programming skills in Python and/or R
• Proficiency with machine learning frameworks like PyTorch or Hugging Face
• Experience with large-scale biomedical datasets
• Proficiency with workflow management systems like Snakemake or Nextflow
• Familiarity with cloud or HPC environments (GCP, AWS, SLURM)

Preferred Qualifications

• Understanding of federated learning and privacy-preserving analysis
• Experience with clinical data harmonization (OMOP CDM, HL7 FHIR)
• Experience designing scalable bioinformatics pipelines
• Experience with regulatory and data governance in clinical research

About the Company

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. As Canada's top research hospital, UHN is a national and international source for discovery, education, and patient care, with major research in neurosciences, oncology, and genomic medicine.