Machine Learning for Drug Discovery Post-Doctoral Research Fellow

Dedicated exclusively to children and their families, The Hospital for Sick Children (SickKids) is one of the largest and most respected paediatric healthcare centres in the world. As innovators in child health, we lead and partner to improve the health of children through the integration of healthcare, leading-edge research and education. Our reputation would not have been built - nor could it be maintained - without the skills, knowledge and experience of the extraordinary people who come to work here every day. SickKids is committed to ongoing learning and development, and features a caring and supportive work environment that combines exceptionally high standards of practice.

When you join SickKids, you become part of our community. We share a commitment and determination to fulfill our vision of Healthier Children. A Better World.

Don't miss out on the opportunity to work alongside the world's best in paediatric healthcare.

The position is under the supervision of Dr. Michal Koziarski within the Molecular Medicine program at The Hospital for Sick Children, University of Toronto, and the 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 drug discovery. We are seeking a candidate interested in fundamental ML research driven by real-world applications.

The role will focus on developing generative models for small molecule design. The candidate will contribute to the development of chemistry-informed generative models using frameworks such as generative flow networks, reinforcement learning, diffusion, and flow matching, guided by practical considerations of high-throughput chemical synthesis. In addition to algorithm development, the candidate will have the opportunity to apply these methods in multidisciplinary projects bridging computational design and experimental validation in the wet lab.

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.
• Access to dedicated computational resources at SickKids Research Institute and the Vector Institute cluster.
• Engagement with the Vector Institute and Acceleration Consortium ecosystems.
  
  

Here's What You'll Need

• 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.
• A commitment to understanding and aiding in the pursuit of equity, diversity and inclusion objectives.
  
  

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.
  
  

Employment Type: Full-time temporary 1 year contract

SickKids is committed to championing equity, diversity and inclusion in all that we do, fostering an intentionally inclusive and culturally safe environment that reflects the diversity of the patients, families and communities we serve. Learn more about workplace inclusion.

If you require accommodation during the application process, please reach out to our aSKHR team. SickKids can provide access and inclusion supports to eligible candidates to support their full engagement during the interview and selection process as well as to ensure candidates are able to perform their duties once successfully hired. If you are invited for an interview and require accommodation, please let us know at the time of your invitation to interview. Information received related to access, inclusion or accommodation will be addressed confidentially.

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