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
Robust written and verbal communication skills

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

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