University Health Network (UHN) is looking for an experienced professional to fill the key role of Postdoctoral Research Fellow (PDF) in the research department at KITE – Toronto Rehabilitation Research institute.
Transforming lives and communities through excellence in care, discovery and learning.
The University Health Network, where “above all else the needs of patients come first”, encompasses Toronto Rehabilitation Institute, Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre and the Michener Institute of Education at UHN. The breadth of research, the complexity of the cases treated, and the magnitude of its educational enterprise has made UHN a national and international resource for patient care, research and education. With a long tradition of groundbreaking firsts and a purpose of “Transforming lives and communities through excellence in care, discovery and learning”, the University Health Network (UHN), Canada’s largest research teaching hospital, brings together over 16,000 employees, more than 1,600 physicians, 8,000+ students, and many volunteers. UHN is a caring, creative place where amazing people are amazing the world.
University Health Network (UHN) is a research hospital affiliated with the University of Toronto and a member of the Toronto Academic Health Science Network. The scope of research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. Research across UHN’s seven research institutes spans the full spectrum of diseases and disciplines, including cancer, cardiovascular sciences, transplantation, neural and sensory sciences, musculoskeletal health, rehabilitation sciences, and community and population health. Find out about our purpose, values and principles here.
The overall goal of the project is to develop a highly personalized care approach through the development of an artificial intelligence driven virtual cardiac rehabilitation system to automatically detect patient engagement, assess exercise technique (resistance training) and predict risk of dropout using the audio/video analysis of patient-clinician interactions. In order to train models to detect patient engagement, two types of data labeling strategies will be performed (i) self reports by the patients using a standardized user engagement scale, and (ii) video annotation. There exist few non-standardized annotation tools to label engagement from videos. The majority of the video annotation tools exist in the online learning setting, and no such tool exists in the realm of virtual rehabilitation. The main role of the PDF is to develop a statistically valid video (and/or audio) annotation scale or adapt an already existing annotation scale(s) to the cardiac rehabilitation population. This scale will be the foundation to train the artificial intelligence models to detect patient engagement.
The applicant will be jointly co-supervised by Scientists from KITE and the Rumsey Cardiac Centre. The successful candidate will jointly work on this project with a research team comprising other postdoctoral fellows, graduate students and research staff. This unique role will involve extensive literature review from multiple disciplines, including computer science, education / behavioral / clinical psychology, social and health science. The successful candidate will lead the execution of study and data collection, develop the engagement annotation scale and validate the artificial intelligence models for engagement detection. The candidate will publish in high impact journals and present at conferences to advance in the field. The postdoctoral fellow will also facilitate development of research ethics protocols, collaboration with interdisciplinary and patient partners, take a leadership role in writing competitive grants, supporting related projects and mentoring trainees within the research team.
- A PhD awarded within 5 years in Computer Science/Cognitive Science, with background in statistics, educational psychology, student/people engagement, and online/distant learning
- Strong publication record in high quality multidisciplinary journals/conferences, including computer vision, speech analysis, deep learning and social sciences/psychology
- Prior experience working within a clinical or healthcare setting will be an asset
- Excellent verbal and written communication skills
- Excellent organizational skills and demonstrated strong leadership skills
Vaccines (COVID and others) are a requirement of the job unless you have an exemption on a medical ground pursuant to the Ontario Human Rights Code.
Posted Date: December 16, 2021 Closing Date: Until filled
More information and apply here.