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. 

www.uhn.ca 

Union: Non-Union
Site: Toronto General Hospital
Department: AI Collaborative Centre
Reports to: Dr. Bo Wang
Work Model: Hybrid
Hours: 20
Salary: 25.00 - 30.00 per hour
Status: Temporary Part Time
Closing Date: November 30, 2025

Position Summary 

Computational prediction of protein function remains a major challenge. High-throughput sequencing generates vast numbers of protein sequences, but only a small fraction have experimentally validated Gene Ontology (GO) annotations. The CAFA 6 competition (Critical Assessment of Functional Annotation) is the leading international benchmark for GO-based function prediction, similar to the CASP challenge in structure prediction that led to breakthroughs such as AlphaFold. Yet, unlike structure prediction, protein function prediction remains unsolved and is a key frontier in computational biology.

Large, self-supervised protein-language models such as ESM-2 and ESM-3 have transformed representation learning by capturing evolutionary, biochemical, and structural semantics. Building on these advances, models such as InterLabelGO+, DPFunc, and PhiGnet have achieved strong benchmark performance in large-scale GO function prediction. Despite progress, current methods still struggle to fuse diverse data modalities, capture hierarchical GO complexities, and generalize to rare or highly specific protein functions.

Duties

• Curate, process, and integrate protein data from CAFA and public bioinformatics databases (e.g., UniProt, InterPro, PDB, Pfam, STRING)
• Implement and fine-tune deep learning architectures (e.g., transformers, graph neural networks) using PyTorch for protein function prediction
• Conduct ablation and benchmarking experiments to evaluate model generalization across organisms and rare functions
• Collaborate with the mentors to design, train, and validate models in an iterative development loop guided by quantitative metrics of CAFA 6
• Maintain reproducible workflows, version control, and thorough documentation of experiments and datasets
• Contribute to competition reports, research abstracts, or manuscripts summarizing project outcomes

• Must be 16 years of age or older, per UHN policy
• Must be enrolled in an undergraduate or postgraduate program in Computer Science, Computational Biology, Biomedical Engineering, Data Science, or a related field
• Strong programming skills in Python and experience implementing and training deep learning models in PyTorch
• Background/experience with bioinformatics and/or computational biology
• Familiarity with the transformer architecture and prior work with LLMs or model fine-tuning
• Experience deploying or adapting models from GitHub/HuggingFace repositories
• Working knowledge of bash, git, virtual environments, and ComputeCanada/SciNet or similar HPC systems
• Excellent problem-solving skills and ability to work independently and in a team environment
• Strong analytical and communication skills, with the ability to present research findings effectively

Why join UHN?

In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN. 

• Competitive offer packages  
• Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP https://hoopp.com/) 
• Close access to Transit and UHN shuttle service 
• A flexible work environment  
• Opportunities for development and promotions within a large organization 
• Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.) 

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.  

All applications must be submitted before the posting close date.

UHN uses email to communicate with selected candidates.  Please ensure you check your email regularly.

Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.

UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.

We thank all applicants for their interest, however, only those selected for further consideration will be contacted.