Assistant Professor - Machine Learning
Date Posted: 12/09/2020
Closing Date: 01/28/2021, 11:59PM EDT 
Req ID: 1499
Job Category: Faculty - Tenure Stream (continuing)
Faculty/Division: Faculty of Arts & Science and Faculty of Applied Science & Engineering
Department: Dept of Computer Science and Dept of Electrical and Computer Engineering
Campus: St. George (Downtown Toronto) 

Description: 

The Department of Computer Science in the Faculty of Arts & Science and the Edward S. Rogers Sr. Department of Electrical & Computer Engineering in the Faculty of Applied Science & Engineering at the University of Toronto invite applications for a joint full-time tenure stream position in the area of Machine Learning with a focus on Deep Learning. The successful candidate’s primary department will depend upon their field of expertise. The appointment will be at the rank of Assistant Professor, and will commence on July 1, 2021, or shortly thereafter.

Applicants must have a Ph.D. in Computer Science, Electrical and Computer Engineering, or a related field, by the date of appointment or shortly thereafter, and demonstrate a strong record of excellence in research and a strong commitment to excellent teaching.

We are especially interested in candidates with expertise in deep learning with a focus on applications to one or more engineering research themes, such as but not limited to, data analytics and intelligent systems, sustainability and engineering, and systems and human health.

We welcome exceptional candidates who transcend traditional backgrounds or discipline boundaries, and candidates whose research and teaching interests complement our existing strengths in the Department of Computer Science and the Department of Electrical and Computer Engineering. 

Evidence of excellence in research will be demonstrated by a record of contributions and publications in top-ranked field-relevant journals, or forthcoming publications meeting high international standards, the submitted research statement, presentations at significant conferences, awards and accolades, and strong endorsements by referees of top international stature.

Candidates will also be expected to demonstrate a strong commitment to excellent teaching at the undergraduate and graduate levels through a teaching statement highlighting previous experience that can include leading successful workshops or seminars, student mentorship, delivering conference presentations or posters, or experience as a teaching assistant or course instructor. A commitment to excellence in teaching may also be demonstrated through materials such as sample course syllabi (either of courses delivered by the candidate or planned for the future), course evaluations, or other evidence of superior performance in teaching-related activities submitted as part of the application. Professional or lived experiences that enhance the ability to teach a diverse student body are highly valued.

At the University of Toronto, the creation of an equitable, diverse, and inclusive community is incumbent on every member of the community (see https://tinyurl.com/UofTEDI). Evidence of a commitment to equity, diversity, inclusion (EDI), and the promotion of a respectful and collegial learning and working environment will weigh favourably on the application. Such evidence may be demonstrated through the application materials, or in a separate statement covering topics such as (but not limited to): research or teaching that incorporates a focus on underrepresented communities, the development of inclusive pedagogies, or the mentoring of students from underrepresented groups.

The candidate will also be reviewed by the Vector Institute for Artificial Intelligence for consideration of appointment as a Faculty Member or Affiliate Faculty Member, and may be nominated for a Canada CIFAR AI Chair through the Vector Institute.

Eligibility and willingness to register as a Professional Engineer in Ontario is highly desirable.

Salary will be commensurate with qualifications and experience, and is competitive with our North American peers.

Application materials for the position must be submitted online through AcademicJobsOnline at https://academicjobsonline.org/ajo/jobs/17705. The required materials are the candidate's curriculum vitae, list of publications, research statement, teaching statement, and at least three letters of reference (on letterhead, signed and scanned) uploaded to AcademicJobsOnline directly by the writers.   

For more information about this position please see our websites (www.cs.toronto.edu, www.ece.toronto.edu, vectorinstitute.ai) or contact recruit@cs.toronto.edu. 

Concurrent with this search, the University of Toronto is undertaking a series of hires in the area of Deep Learning in recognition of University Professor Emeritus Geoffrey Hinton’s winning of the prestigious A.M. Turing Award in 2019. These searches are in the Department of Computer Science, and joint between the Department of Computer Science and the Edward S. Rogers Sr. Department of Electrical & Computer Engineering.

The Department of Computer Science and the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto rank among the best in North America.

Both Departments attract outstanding students, have excellent facilities, and are ideally located in one of the most vibrant, artistic, diverse and cosmopolitan cities in the world. Additional information may be found at http://www.cs.utoronto.ca and http://www.ece.utoronto.ca. 

Review of applications will begin on January 13, 2021; however, the position will remain open until January 28, 2021.

Caution: This ad is “posted only” to the U of T faculty job board. Please see the information above for the application instructions. Applications submitted via the U of T platform will NOT be considered for  this position.

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, LGBTQ persons, and others who may contribute to the further diversification of ideas.

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.