Ville : Toronto

Catégorie : Full Time

Industrie : Healthcare

Employeur : The Hospital for Sick Children

About Sickkids

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.

Posting Description

Location: Toronto, Ontario, Canada (Split between The Hospital for Sick Children and the Structural Genomics Consortium).
Term: Multi-year project (Estimated 2026-2028).
Supervision: Dr. Michal Koziarski (SickKids/UofT) and Dr. Matthieu Schapira (SGC/UofT).
Industry partnership: A Senior Scientific Director, Computational Drug Discovery from a large pharmaceutical company agreed to act as Industry Mentor.

We are seeking a highly motivated Post-Doctoral Fellow to lead a cutting-edge project focused on advancing machine learning (ML) methodologies for the accurate prediction of small-molecule binding affinity. This project addresses the fundamental challenges of data scarcity and generalization in drug discovery by leveraging large-scale datasets from DNA-encoded library (DEL) and affinity selection mass spectrometry (ASMS) platforms.

As a Mitacs Target 2035 post-doctoral fellow, you will work in an interdisciplinary team with academic, SGC, and industry scientists and benefit from training supported through Mitacs.

Core Objectives

You will be responsible for executing two tightly integrated research objectives:

1. Multi-fidelity binding affinity prediction pipelines: develop efficient strategies for integrating existing foundation ML models with target-specific experimental screening data and computationally intensive physics-based methods.
2. Novel foundation ML models trained on DEL/ASMS data: develop novel foundation ML models capable of learning transferable representations of molecular recognition directly from DEL and ASMS datasets spanning hundreds of proteins. These models will support generalizable predictions for unseen proteins and ligands.

Role Responsibilities

• Benchmarking: Systematically evaluate existing foundation models (e.g., Boltz-2, AQAffinity, Uni-Mol) for binding affinity prediction.
• Model Engineering: Develop novel fine-tuning strategies tailored to DEL/ASMS datasets.
• Pipeline Integration: Implement iterative workflows where ML predictions guide the selection of compounds for hit-to-lead optimization.
• Dissemination: Produce at least two peer-reviewed publications and assemble an open-source computational library integrating foundation ML models and multi-fidelity optimization components.

Qualifications

• PhD in Computational Chemistry, Computer Science, Bioinformatics, or a related field.
• Technical Expertise: Strong background in representation learning, foundation models, and multi-fidelity optimization.
• Domain Knowledge: Familiarity with physics-based molecular modeling (ex: FEP).
• Skills: Experience with large-scale ML training, handling "noisy" experimental data (like DEL).

Open Science: This project operates within an open science framework, ensuring your work has a high impact on the global drug discovery community.

Our Commitment to Diversity

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.

Accessibility & Accommodation

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.

How to Apply

Technical difficulties? Email ask.hr@sickkids.ca with a short description of the issues you are experiencing. We will not accept resumes sent to this inbox but we are happy to respond to requests for technical assistance.

Tip: Combine your cover letter and resume into ONE document of 20 pages or less as you cannot upload multiple documents as part of your application.

Every application is reviewed by a human recruiter and all hiring decisions are made by people. In some cases, AI-assisted tools are used to help review applications based on job-related qualifications.

All positions posted on the SickKids Hospital's Careers Site represent current vacancies, unless otherwise posted in the job description.