Sessional Lecturer - APS360H1 F Applied Fundamentals of Deep Learning
Toronto, ON, CA
University of Toronto
The University of Toronto is a globally top-ranked public research university in Toronto, Ontario, Canada.Date Posted: 06/28/2024
Req ID: 38456
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: 2 sections with enrolment of 100 each
Estimated TA support: 300 hrs per section
Course Schedule:
LEC0101 - Mon/Thurs 6:00-8:00 p.m..
LEC0102 - Mon 9-11 a.m./ Tues 10 a.m.-12:00 p.m.
Sessional dates of appointment: Sept 1, 2024- Dec 31, 2024
Stipend: $16,710 (inclusive of vacation pay)
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 final exam 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.
Closing Date: July 19, 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
Associate Director
Cross-Disciplinary Programs Office
Faculty of Applied Science and Engineering
email: s.brown@utoronto.ca
Closing Date: 07/19/2024, 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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Computer Vision Deep Learning Engineering GANs Machine Learning NLP PyTorch Reinforcement Learning Teaching
Perks/benefits: Career development
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