Postdoctoral Researcher: Structural Biology

Company Description

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care.

UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

Job Description

Union: Non-Union
Number of Vacancies: 1
New or Replacement Position: New
Site: Princess Margaret Cancer Centre
Department: Research
Reports to: Senior Scientist
Hours: Hours Per Week
Salary Range: $54,902 - $71,372 Annually
Shifts: Monday - Friday
Status: Temporary Full-time
Closing Date:
March 16, 2026

Program: Mitacs Accelerate Umbrella - Target 2035 Fellows

Position Summary
:
We are seeking a highly motivated Postdoctoral Researcher with experience in X-ray Crystallography to join an ambitious, data-driven drug discovery program at the interface of structural biology, chemical biology, and artificial intelligence. This role will play a central part in hit characterisation for , generating high-quality protein-ligand co-crystal structures that directly inform structure-based drug discovery research programs, the development of machine learning models and enable international benchmarking challenges.

The Postdoctoral Researcher will focus on structure determination of protein-ligand complexes spanning key biological pathways including epigenetics, ubiquitin signalling, gene regulation, and DNA repair. These structures will not only drive internal hit validation and SAR development but will also feed into global open science efforts and benchmarking initiatives such as CASP, helping define the next generation of AI-enabled drug discovery.

This is an exciting opportunity to work at the frontier of structural biology and AI, where experimentally rigorous crystallographic data are treated as first-class training data for machine learning. The successful candidate will contribute to a growing portfolio of high-impact structures designed to stress-test, validate, and improve LIGAND-AI predictions in real-world drug discovery settings.

The Postdoctoral Researcher will be embedded in a highly collaborative early-stage drug discovery team, working closely with medicinal chemists, computational scientists, machine learning experts, automation engineers, and biochemists. Together, the team integrates structure, chemistry, and data science to rapidly advance hit matter and generate benchmark-quality datasets with broad community value.

The positions offer a highly collaborative environment, access to state-of-the-art infrastructure, and the opportunity to contribute to research addressing a major unmet medical and societal need.

Appointment, Training Program, and Research Environment

This position is part of the Target 2035 Fellows program, supported through a Mitacs Accelerate Umbrella award. The appointment is a temporary full-time postdoctoral position for an initial one-year term, with the possibility of renewal for up to two additional years, subject to performance, funding availability, and program requirements.

The Postdoctoral Researcher will be based at University Health Network (UHN) within the Princess Margaret Cancer Centre research environment and will contribute to Target 2035, a global public-private partnership dedicated to accelerating early drug discovery through the open generation of high-quality protein-ligand interaction data for AI/ML.

Training will occur through close academic…