: Research Internship in Machine Learning and Bio-informatics

Research Internship in Machine Learning and Bio-informatics

Job Posting # 925084

Union : Non-Union

Site : Toronto General Hospital Research Institute

Department : Schroeder Arthritis Institute

Reports to : Senior Scientist

Hours : 25 hours per week

Salary : $25 CAD per hour (salary commensurate with candidate experience and consistent with UHN Compensation Policy)

Status : Temporary Part Time

Shifts : Days

Posted Date : September 29, 2023

Closing Date : October 29, 2023

The University Health Network, where "above all else the needs of patients come first", encompasses Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute and the Michener Institute of Education. 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.

Position Summary

Toronto General Research Institute at University Health Network and i4Health Research Lab at York University are seeking applications for a Research Internship with an interest in building new innovative methods in Machine Learning (ML) and deep learning for biomarker discovery. This is an exciting and interdisciplinary opportunity to work alongside top clinicians and AI scientists to develop groundbreaking ML tools. You will explore a wide range of topics, including but not limited to deep representation learning, multimodal learning, and analyzing multi-omics data to identify patterns or pathways of gene activity that could help explain clinical presentations, immune system abnormalities, and observable characteristics in individuals experiencing post-COVID-19 conditions.

Duties

• Conduct literature reviews to understand the latest research in the application of ML to multi-omics and biological data in post-COVID-19 condition and identify relevant papers and approaches
• Develop ML models to analyze, visualize, and build low-dimensional representations of biological sequence data associated with patients with post-COVID-19 condition
• Use ML models to combine clinical and biological data resulting in the categorization of patient subtypes for post-COVID-19 conditions
• Analyze the results of ML models, interpret findings, and visualize outcomes
• Disseminate the findings and outcome of the analysis and research experiments at high-impact journals and conferences

Qualifications :

Nice to have :

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.

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.

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.

UHN is a respectful, caring, and inclusive workplace. We are committed to championing accessibility, diversity and equal opportunity. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known in advance. Any information received related to an accommodation will be addressed confidentially.

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