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
- Strong 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 in Toronto employer: University Health Network
University Health Network (UHN) is an exceptional employer that fosters a collaborative and innovative work culture, making it an ideal place for professionals passionate about advancing healthcare through AI-driven research. Located in the heart of Toronto, UHN offers employees access to cutting-edge resources, professional development opportunities, and a commitment to impactful work that enhances patient care. Join us to be part of a dynamic team dedicated to transforming the future of theranostics and clinical trials.
