Job Description

Date Posted: 05/04/2023
Closing Date: 06/15/2023, 11:59PM ET
Req ID: 30842
Job Category: Faculty - Teaching Stream, Contractually Limited Term Appointment
Faculty/Division: Faculty of Applied Science & Engineering
Department: Edward S. Rogers Sr. Department of Electrical and Computer Engineering
Campus: St. George (Downtown Toronto)
Description:
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) in the Faculty of Applied Science and Engineering (FASE) at the University of Toronto invites applications for a three-year Contractually Limited Term Appointment (CLTA) in the field of artificial intelligence and deep learning. The appointment will be at the rank of Assistant Professor, Teaching Stream, with an anticipated start date of September 1, 2023, with the possibility of a two-year extension.
Applicants must have earned a PhD degree in electrical and computer engineering or a related area by the time of appointment, or shortly thereafter with a demonstrated record of excellence in teaching. We seek candidates whose teaching interests complement and enhance our existing departmental strengths, and address the needs of a growing professional masters degree program. Candidates must have teaching experience in a degree granting program, including lecture preparation and delivery, curriculum development, and development of online material/lectures. Additionally, candidates must possess a demonstrated commitment to excellent pedagogical inquiry and a demonstrated interest in teaching-related scholarly activities at the graduate-level.
We seek candidates with teaching skills and expertise in the development and deployment of deep learning-based software and applications. This includes knowledge of the fundamental theory of deep learning along with significant experience in the development of software applications that employ neural network and deep learning approaches to computer vision, natural language processing, and the use of reinforcement learning. The candidate should have experience in the deployment of industrial applications of deep learning. Graduate-level subjects to be taught include core deep learning/software, machine learning \xe2\x80\x9cOPs\xe2\x80\x9d in production, introduction to reinforcement learning and data science methods/quantitative analysis. Experience with biomedical applications of deep learning would be an asset. Candidates are expected to be able to teach courses in existing curricula and to create some new courses, and to demonstrate their ability to teach.
Evidence of excellence in teaching and a commitment to excellent pedagogical inquiry can be demonstrated through teaching accomplishments, awards and accolades, presentations at significant conferences, the teaching dossier submitted as part of the application (with required materials outlined below) as well as strong letters of reference from referees of high standing familiar with relevant aspects of the applicant\xe2\x80\x99s work.
Equity, diversity, and inclusion (EDI) are essential to academic excellence and to the success of our department. Evidence of a commitment to EDI will be demonstrated by a statement describing views, experiences and/or plans furthering EDI via student mentorship, pedagogy, outreach, and/or other activities.
Salary is commensurate with qualifications and experience.
All qualified candidates are invited to apply online by clicking the link below. Applicants must submit a cover letter outlining the suitability to teach at the graduate level; a current detailed curriculum vitae; and a complete teaching dossier including a strong teaching statement, sample syllabi and course materials, and teaching evaluations; and an EDI statement.
Applicants must provide the name and contact information of three references. The University of Toronto\xe2\x80\x99s recruiting tool will automatically solicit and collect letters of reference from each once an application is submitted (this happens overnight). Applicants remain responsible for ensuring that references submit letters (on letterhead, dated and signed) by the closing date. At least one reference letter must primarily address the candidate\xe2\x80\x99s teaching. More details on the automatic reference letter collection, including timelines, are available in the FAQ\xe2\x80\x99s.
Submission guidelines can be found at http://uoft.me/how-to-apply. Your CV and cover letter should be uploaded into the dedicated fields. Please combine additional application materials into one or two files in PDF/MS Word format. If you have any questions about this position, please contact Inna Latypova at eceoffice@utoronto.ca.
All application materials, including reference letters, must be received by June 15, 2023. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.
Diversity Statement
The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.
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.