Careers at UHN

Job Description

Post-Doctoral Researcher in Machine Learning - Toronto, Ontario

Job Posting# 913073

Position: Post-Doctoral Researcher in Machine Learning
Site:  Toronto General Hospital Research Institute
Department:  Multi Organ Transplant
Reports to: Principle Investigator      
Hours: 37.5 hours per week  
Status:  Temporary full time - 1 year

University Health Network (UHN) is looking for an experienced professional to fill the key role of Postdoctoral Researcher in our Multi Organ Transplant Department.

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 ground breaking 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 1,200 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 five 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.

We seek Post-Doctoral Research applications for a project involving the application of cutting-edge ML tools to healthcare. The project will focus on how to leverage ideas from deep learning, causal inference and predictive models to develop software for clinicians from large patient datasets with longitudinal clinical, laboratory and molecular data. The candidate would be co-supervised by both Dr. Mamatha Bhat (clinician) and Dr. Rahul G. Krishnan (computer scientist).

Required qualifications

Minimum Phd in computational biology, computer science, engineering, or statistics. Expertise in Python, C/C++ and Unix programming environments. We encourage PhD applicants interested in exploring this intersection space to apply (the opportunity can be converted into a postdoctoral fellowship).

Preferred qualifications

Hands-on experience using machine learning tools such as scikit-learn as well as developing custom machine learning models in PyTorch/JAX/Tensorflow in high performance python computing environments.


We will accept applications until the position is filled. Please submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references with your application. All documents should be provided in PDF.


Selected publications:

Nitski O*, Azhie A*, Qazi-Arisar FA, Wang X, Ma S, Lilly L, Watt KD, Levitsky J, Asrani SK, Lee DS, Rubin BB, *Bhat M, *Wang B. Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data. Lancet Digit Health. 2021 May;3(5): e295-e305.

Yasodhara A, Dong V, Azhie A, Goldenberg A, Bhat M. Identifying Modifiable Predictors of Long-Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning. Liver Transpl. 2021 Apr;27(4):536-547.

Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020 Mar;71(3):1093-1105.

Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med. 2022 Jul

Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int. 2022 Jun;16(3):495-508. doi: 10.1007/s12072-021-10291-7. Epub 2022 Jan 12. PMID: 35020154.

Sharma D, Gotlieb N, Farkouh ME, Patel K, Xu W, Bhat M. Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort. J Am Heart Assoc. 2022 Jan 4;11(1):e022576. doi: 10.1161/JAHA.

Cooper M, Krishnan RG, Bhat M. Deep learning and the future of the Model for End-Stage Liver Disease-sodium score. Liver Transpl. 2022 Jul;28(7):1128-1130. doi: 10.1002/lt.26485. Epub 2022 May 4. PMID: 35437897.

Bhat M, Rabindranath M. The promise of artificial intelligence for predictive biomarkers in hepatology. Hepatol Int. 2022 Jun;16(3):523-525. doi: 10.1007/s12072-022-10342-7. Epub 2022 May 16. PMID: 35575965.

 Sarvestany SS, Kwong JC, Azhie A, Dong V, Cerocchi O, Ali AF, Karnam RS, Kuriry H, Shengir M, Candido E, Duchen R, Sebastiani G, Patel K, Goldenberg A, Bhat M. Development and validation of an ensemble machine learning framework for detection of all-cause

Vaccines (COVID-19 and others) are a requirement of the job unless you have an exemption on a medical ground pursuant to the Ontario Human Rights Code.

Closing Date: Until filled

For current UHN employees, only those who have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, and possess all the required experience and qualifications should apply.

University Health Network thanks all applicants, however, only those selected for an interview will be contacted.

UHN is a respectful, caring, and inclusive workplace. We are committed to championing accessibility, diversity and equal opportunity and welcomes all applicants including but not limited to: all religions and ethnicities, LGBTQ2s+, BIPOC, persons with disabilities and all others who may contribute to the further diversification of ideas. Requests for accommodation can be made at any stage of the recruitment process providing the applicant has met the Bona-fide requirements for the open position. Applicants need to make their requirements known when contacted.

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