Develop deep learning models for drug discovery at UHN’s Structural Genomics Consortium, working with DNA-Encoded Library and biomedical data.

This Data Scientist position focuses on identifying and optimizing drug candidates for challenging protein targets. You will build predictive systems using Python and deep learning frameworks, while collaborating with the Haibe-Kains and Schapira labs. Your work contributes to open science and pre-competitive research.

Key Responsibilities:

- Prioritize drug-like compounds for defined protein targets
- Fine-tune deep learning architectures for molecular properties
- Support de-novo drug design with generative models
- Translate scientific questions into machine learning solutions




- Present findings to technical and non-technical stakeholders

Requirements:

- Proven machine learning experience in drug discovery or biomedical domains
- Proficiency with TensorFlow, PyTorch, or Keras
- Experience building transformers or graph neural networks
- Understanding of feature extraction and evaluation metrics
- Solid written and verbal communication skills

Use deep learning, molecular modeling, and scientific communication to advance UHN’s computational drug discovery work.

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📌 Machine Learning Drug Discovery Scientist (Toronto)
🏢 University Health Network
📍 Toronto