Postdoctoral Fellow – Machine Learning for Drug Discovery

The position is under the supervision of Dr. Michał Koziarski within the Molecular Medicine program at the Hospital for Sick Children, University of Toronto, and Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and active learning to efficiently explore chemical space, with applications in anti-aging intervention discovery. We seek a candidate interested in fundamental ML research driven by real-world applications and aligned with the goal of extending healthy human lifespan.

The role will center on developing generative models for small molecule design and applying them to various longevity-related projects, including (but not limited to) the discovery of novel senolytics. The candidate will contribute to the development of novel methods that integrate ML with chemistry, emphasizing cost and speed of chemical synthesis. While we specialize in computer science and algorithm development, our goals go beyond just improving benchmark results – we’re focused on designing new therapeutics that help people live longer, healthier lives. As a result, our research is highly interdisciplinary, bridging ML with experimental work and fostering close collaborations with biologists and chemists at SickKids, the University of Toronto, and the Acceleration Consortium.

What We Offer:

• An interdisciplinary environment with expertise and close collaborations spanning core ML, chemistry, and biology.
• ML research fundamentally motivated by the most pressing biological questions.
• Flexibility in shaping research directions in alignment with the group’s mission and the candidate’s interests.
• High bandwidth in supervision: as a newly established group, we have the capacity to closely support and mentor the successful candidate.
• Access to dedicated computational resources at SickKids Research Institute and the Vector Institute cluster.
• Engagement with the Vector Institute and Acceleration Consortium ecosystems.

Requirements:

• PhD in a machine learning-related discipline.
• Strong publication track record in top ML, computational chemistry, or computational biology conferences and journals.
• Proficiency in Python, demonstrated through completed projects, preferably with publicly available repositories.
• Experience with deep learning frameworks such as PyTorch and JAX.
• Hands-on experience in developing and training deep learning models.
• Familiarity with reinforcement learning and/or generative models.

Preferred Qualifications:

• Experience in drug discovery and chemical data.
• Background in generative models for small molecule design.
• Experience with GFlowNets.
• Familiarity with active learning approaches.

Prospective candidates are encouraged to reach out directly to Dr. Michał Koziarski to discuss potential research directions at michal.koziarski@sickkids.ca.

Tagged as: drug discovery, generative models, machine learning, senescence