Date Posted: 06/29/2022
Req ID: 25610
Faculty/Division: Faculty of Applied Science & Engineering
Department: Cross-Disciplinary Programs Office
Campus: St. George (Downtown Toronto)
Description:
Course title: APS360H1 F – Applied Fundamentals of Deep Learning
Course description: A basic introduction to the history, technology, programming and applications of the fast evolving field of deep learning. Topics to be covered may include neural networks, autoencoders/decoders, recurrent neural networks, natural language processing, and generative adversarial networks. Special attention will be paid to fairness and ethics issues surrounding machine learning. An applied approach will be taken, where students get hands-on exposure to the covered techniques through the use of state-of-the-art machine learning software frameworks.
Estimated enrolment: 100
Estimated TA support: 300 hrs
Course Schedule:
Section 1)Monday/Wednesday 7:00-9:00 p.m.
Section 2) Tuesday/Thursday 3:00-5:00 p.m.
Sessional dates of appointment: September 1, 2022- December 31, 2022
Stipend: $15,000
Qualifications: Ph.D. or equivalent professional experience in the area of AI and Machine Learning; P.Eng. preferred. Knowledge of, and experience with deep neural networks and their application to computer vision and other pattern recognition problems is required. Knowledge of forward-looking AI/ML concepts, such as reinforcement learning and recurrent neural networks, preferred. Hands-on experience with PyTorch highly desirable. Familiarity with fairness and ethics issues surrounding AI. Previous experience teaching a similar course is highly desirable. Familiarity with engineering concepts and engineering education is an asset.
Duties: The lecturer will prepare for and deliver 13 weeks of lectures and tutorials; set assignments and term work assessments and in-person final assessment as appropriate; collate and submit marks; handle petitions after final marks have been submitted; communicating with students both inside and outside of class times. Preparation of a deferred examination may also be required. Delivery of this course is intended to be in-person but may be online or a hybrid as dictated by Public Health guidelines.
Closing Date: July 21, 2022, 11:59 p.m..
Those interested should submit a cover letter, current cv detailing teaching experience, and CUPE 3902 Unit 3 application form by email to:
Sharon Brown
Assistant Director
Cross-Disciplinary Programs Office
Faculty of Applied Science and Engineering
email: s.brown@utoronto.ca
Closing Date: 07/21/2022, 11:59PM EDT
**
This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.
Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.
All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.
Diversity Statement
The University of Toronto is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas.
As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.
Accessibility Statement
The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.
The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.
If you require any accommodations at any point during the application and hiring process, please contact uoft.careers@utoronto.ca.