Research Summer Student 2026

Union: Non-Union 
Number of Vacancies: 1
New or Replacement: New
Site: Toronto Western Hospital
Department: Cardiology
Reports to: Dr. Mali Worme
Hours: 35 hours per week
Salary: $17.60 - $20.91 
Shifts: Monday to Friday
Status: Temporary Full-time
Closing Date: February 27, 2026

Position Summary
This placement is for a summer student, at either the undergraduate or medical student level, with experience in programming, coding, and the application of artificial intelligence to medical imaging. The student will participate in research within the Echocardiography Laboratory at University Health Network by extracting echocardiographic studies and working with existing large image models in echocardiography to further develop algorithms that can differentiate between ischemic and non-ischemic cardiomyopathy. Using curated datasets from UHN representing specific non-ischemic cardiomyopathy subtypes (for example sarcoidosis, amyloidosis, Fabry disease, arrhythmogenic right ventricular cardiomyopathy, and iron overload cardiomyopathy etc ), the student will attempt to train existing echo models to differentiate etiologies of non-ischemic cardiomyopathy, a significant clinical dilemma for cardiologists and heart failure specialists. 

Educational Objectives

• To learn the basics of echocardiography nomenclature, standard views, and core image acquisition principles. 
• To expand skills in the use of large image models and related deep learning tools, and to further develop their applications in echocardiography and cardiomyopathy phenotyping. 
• To work closely with the head of the Toronto General Hospital Echocardiography Laboratory and the lab’s Quality lead to gain exposure to Research methodology, including Study design, data curation, and model evaluation. 

Proposed deliverables at the end of the placement

• Development and preliminary testing of a pilot large image model–based algorithm aimed at differentiating ischemic from non-ischemic cardiomyopathy using echocardiographic data. 
• Exploratory analyses and models assessing the potential to distinguish between different etiologies of non-ischemic cardiomyopathy (for example sarcoidosis, amyloidosis, Fabry disease, arrhythmogenic right ventricular cardiomyopathy, and iron overload) based on echocardiographic features. 
• Preparation of an abstract and presentation summarizing the project’s methods, preliminary findings, and future directions for submission to the Ted Rogers Centre for Heart Research Summer Research Day. 

Duties

• Extract, curate, and organize echocardiographic imaging datasets from UHN for research use.
• Apply and adapt existing deep-learning and large image models to echocardiographic data using Python and PyTorch.
• Support the development and preliminary testing of AI-based algorithms to differentiate ischemic and non-ischemic cardiomyopathy.
• Conduct exploratory analyses to assess whether echocardiographic features can distinguish specific non-ischemic cardiomyopathy etiologies.
• Learn and apply core echocardiography concepts, including standard views and nomenclature, to inform model development.
• Collaborate with clinical and research mentors on study design, data curation, and model evaluation.
• Contribute to documentation, abstract preparation, and presentation of findings for the Ted Rogers Centre for Heart Research Summer Research Day.

• Current undergraduate or medical student in a relevant field (for example: computer science, engineering, data science, medicine, or a related discipline).
• Demonstrated experience with Python and PyTorch for machine learning and deep learning projects. 
• Prior involvement in research projects that apply artificial intelligence to medical imaging modalities (for example: echocardiography, cardiac MRI, CT, or other radiologic imaging). 
• Foundational understanding of medical AI development workflows, including data preprocessing, model training, validation, and basic performance evaluation. 
• Strong interest in cardiovascular medicine, echocardiography, and translational applications of AI in clinical care. 

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.