Faculty AI Scientist (Scientist II)

Job description

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. www.uhn.ca Job Description Union: Non-Union Number of vacancies: 1 New or Replacement Position: New Site: Toronto General Hospital Department: Peter Munk Cardiac Centre Reports to: PMCC Research Director, and Executive Lead, PMCC AI Team Hours: 37.5 hours per week Shifts: Days Status: Permanent Full-Time Closing Date: June 16, 2026 Position Summary The PMCC (Peter Munk Cardiac Centre) at University Health Network (UHN), which includes a research institute affiliated with the University of Toronto, seeks a faculty Scientist for a full-time appointment at the Assistant or Associate Professor level, to establish an independent world-class research program in Cardiovascular Artificial Intelligence. The successful candidate will be supported by a generous start-up package, an independent, modern laboratory space, and assistance in obtaining a cross-appointment to the Department of Computer Science at the University of Toronto. A PMCC appointment is expected at the rank of Scientist, on a career path to Senior Scientist over a six-year period (tenure-track-equivalent). It is anticipated that the successful candidate will develop an independent research program with extramural funding and participate in strategic planning for the PMCC AI Team. The successful applicant will help drive the next generation of AI in cardiovascular disease, with a focus on AI applications to improve the management of patients with conditions that can lead to heart failure, including coronary artery disease, heart valve disease, abnormal heart rhythms, and heart enlargement. This is a rare opportunity to work at the forefront of translational AI, developing and deploying advanced algorithms that improve diagnosis, risk prediction, clinical decision-making, workflow, and patient outcomes across cardiovascular care. The successful candidate will join the highly collaborative and exceptionally productive PMCC AI team, which brings together clinicians, AI and data scientists, software developers, machine learning engineers, researchers, and operational leaders across cardiology, cardiac surgery, vascular surgery, critical care, medical imaging, and digital health, and will help ensure that AI tools are safe, auditable, scalable, and aligned with the strategic objectives of the PMCC and UHN. The role is focused on both innovation and implementation, building novel AI solutions using the latest methods and driving their deployment in real-world clinical settings to improve patient outcomes. For more information about the PMCC AI Team, please visit our website at https://pmcc.ai . This Faculty AI Scientist role offers an opportunity to contribute to the next generation of AI in cardiovascular medicine and surgery within a major academic hospital environment. Salary will be commensurate with the successful candidate’s qualifications, experience, academic profile, and programmatic fit. Duties Lead the development, validation, and deployment of AI models for cardiovascular applications using multimodal data, including imaging (ultrasound, CT, MRI), ECG, electronic health records, genomics, and wearable device data. Apply the latest approaches in AI, including deep learning, transformer architectures, foundation models, multimodal learning, self-supervised learning, generative AI, agentic workflows, and large language models, where appropriate. Work closely with clinicians and operational leaders to identify high-value problems in cardiovascular medicine and surgery and translate them into ethical, scalable AI solutions that are deployed and evaluated in a real-world healthcare environment. Assemble a dedicated, cross-functional team that owns the end-to-end delivery of AI initiatives from problem framing to deployment that includes data scientists, ML and data engineers, AI ethicists, MLOps/DevOps engineers, and UX designers. Build reproducible pipelines for data curation, model training, evaluation, monitoring, and continuous improvement. Support prospective validation, clinical integration, and post-deployment monitoring of algorithms in clinical practice. Lead publications, grants, and presentations, and contribute to the growth of a leading cardiovascular AI program. Supervise trainees and collaborate across scientific, clinical, and industry partnerships. Qualifications A PhD or equivalent advanced degree in computer science, machine learning, biomedical engineering, data science, statistics, or a related field. Strong expertise in modern AI and machine learning methods, with hands-on experience developing state-of-the-art models. Strong programming skills, especially in Python, and experience with modern ML frameworks and compute environments. Demonstrated success in research, including peer-reviewed publications and/or impactful applied AI work, as well as a track record of peer-reviewed funding. Ability to work effectively in interdisciplinary teams and communicate complex ideas clearly to both technical and clinical audiences. Previous work in cardiovascular medicine, biomedical data science, or healthcare AI preferred. Multimodal data fusion, medical imaging, time-series analysis, NLP, or foundation model development preferred. Real-world deployment of AI in healthcare settings preferred. AI safety, explainability, fairness, governance, and model monitoring preferred. 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 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.