Stage recherche data science h/f (Stage)

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

Qualifications :

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

Additional Information :

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 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 UHNs 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.

Remote Work :

No

Employment Type :

Part-time