Job Title

Assistant Professor, Teaching Stream - Artificial Intelligence and Deep Learning Date Posted: 10/29/2025; Closing Date: 12/03/2025, 11:59PM ET; Req ID: 45222; Job Category: Faculty - Teaching Stream (continuing); Faculty/Division: Faculty of Applied Science & Engineering; Department: Edward S. Rogers Sr. Department of Electrical and Computer Engineering; Campus: St. George (Downtown Toronto) Overview The Edward S. Rogers Sr. Department of Electrical and Computer Engineering (ECE) in the Faculty of Applied Science & Engineering at the University of Toronto invites applications for a full-time teaching stream position in the area of Artificial Intelligence and Deep Learning. The appointment will be at the rank of Assistant Professor, Teaching Stream, with an anticipated start date of July 1, 2026. Responsibilities Possess teaching experience in a degree-granting program, including lecture preparation and delivery, curriculum development, and development of online material/lectures. Demonstrate commitment to excellent pedagogical inquiry and teaching-related scholarly activities at the graduate level. Develop and deliver graduate-level courses related to deep learning, machine learning in production, reinforcement learning, data science methods, and quantitative analysis; create new courses as needed and integrate into existing curricula. Teach deep learningbased software and applications, including theory of deep learning and development of software using neural networks for computer vision, natural language processing, and reinforcement learning; deploy industrial applications of deep learning. Possess or be willing to register as a Professional Engineer in Ontario (highly desirable). Qualifications PhD in Electrical and Computer Engineering or a related field by the time of appointment or shortly thereafter, with demonstrated record of excellence in teaching. Alignment of teaching interests with departmental strengths and the needs of a growing professional masters program. Evidence of excellence in teaching and a strong teaching dossier, including letters of reference from referees familiar with relevant work. Demonstrated ability to teach courses in existing curricula and to develop new courses; commitment to inclusive pedagogies and equity in teaching environments. Experience with deployment of deep learning in industrial applications; familiarity with deep learning theory and applications in computer vision, NLP, and reinforcement learning; knowledge of biomedical deep learning applications is an asset. Eligibility and willingness to register as a Professional Engineer in Ontario (strongly desirable). Equity, Diversity, and Inclusion (EDI) Evidence of a commitment to EDI must be demonstrated by a statement in the application describing teaching that incorporates inclusive pedagogies, supports underrepresented communities, and plans for mentoring and outreach related to EDI. Application Process Salary will be commensurate with qualifications and experience. All qualified candidates are invited to apply online: include a cover letter, current curriculum vitae, teaching dossier (summary of teaching experience and accomplishments, teaching statement, sample syllabi and materials, teaching evaluations), and an EDI statement. Provide the names and contact information of three references. Referees will be solicited automatically after application submission; ensure letters are submitted by the closing date. At least one letter should address teaching. Submit all materials in PDF/MS Word format; combine additional materials into one or two files. Applications submitted in other ways will not be considered. Questions about the position can be directed to the ECE department via . All application materials, including recent reference letters, must be received by December 3, 2025. Note: Canadians and permanent residents are given priority, but all qualified candidates are encouraged to apply. Diversity Statement The University of Toronto embraces diversity and is committed to a culture of belonging. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. Diversity statements are part of the application process. Accessibility The University strives to be an equitable and inclusive community, with efforts to increase accessibility in recruitment and selection processes. If accommodations are required at any point during the process, please contact . For more information about the Faculty of Applied Science and Engineering, visit Additional information about the department is available at