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Assistant Professor - Statistical Learning Theory

The University of Toronto's Department of Statistical Sciences is recruiting a full-time tenure-stream Assistant Professor in Statistical Learning Theory and adjacent fields, with an anticipated July 1, 2027 start.

Job Full source details
University of Toronto Toronto, Ontario, Canada Verified 4 weeks ago
✓ 100% verification score · Source: University of Toronto Careers · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
DeadlineNovember 9, 2026 at 11:59 PM ET
EmploymentFull-time, tenure stream
Published compensationCAD 140000.00 – 190000.00
CountryCanada

Overview

The University of Toronto's Department of Statistical Sciences is recruiting a full-time tenure-stream Assistant Professor in Statistical Learning Theory and adjacent fields, with an anticipated July 1, 2027 start.

Full job description

The successful candidate is expected to establish an innovative, independent and externally funded research program, teach and contribute service in the Department of Statistical Sciences. The official posting lists an expected salary range of CAD 140,000 to CAD 190,000 per year and states that applications must be submitted through Academic Jobs Online rather than the University of Toronto job-board application button.

Requirements & qualifications

Applicants must have earned a PhD in Statistics, Computer Science, Data Science or a closely related discipline by the time of appointment or shortly thereafter, and demonstrate excellence in research and teaching in Statistical Learning Theory. All qualified candidates are invited to apply; Canadians and permanent residents will be given priority.

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Verification notes

Verified against the official University of Toronto posting. Req ID 48743 is an existing vacancy; the official page directs candidates to Academic Jobs Online and warns that U of T platform submissions will not be considered.

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