Assistant, Associate or Full Professor in Artificial Intelligence or Machine Learning [closing 15.11.20]

Страна: Канада;

Город: Toronto

Добавлена: 27.10.2020

Работодатель: The Department of Electrical Engineering and Computer Science at York University

Тип: PostDoc vacancy;

Для кого: For researchers;

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Дедлайн подачи: 15.11.2020

 

The Department of Electrical Engineering and Computer Science, York University invites highly qualified candidates to apply for a professorial stream tenured or tenure-track appointment in Artificial Intelligence or Machine Learning at an open rank – Assistant, Associate or Full Professor level, depending on experience – to commence July 1, 2021. Salary will be commensurate with qualifications and experience. All York University positions are subject to budgetary approval.

A PhD in computer science or a closely related field is required, with a demonstrated record of excellence, or promise of excellence (depending on the rank of the appointment), in research and in teaching.

Applicants should have a clearly articulated program of research in Artificial Intelligence or Machine Learning, interpreted broadly. Potential areas include, but are not limited to, machine learning theory, computer vision, and natural language processing.

The successful candidate will be expected to engage in outstanding, innovative, and externally funded research at the highest level.

Candidates must provide evidence of research excellence or promise of research excellence of a recognized international calibre, as demonstrated by, for example: the research statement; a record of publications in significant journals in the field; presentations at major conferences; awards and accolades; and recommendations from referees of high standing. The position will involve graduate teaching and supervision, as well as undergraduate teaching, and the successful candidate must be suitable for prompt appointment to the Faculty of Graduate Studies.

Evidence of excellence or promise of excellence in teaching will be provided through means such as: the teaching statement; teaching accomplishments and pedagogical innovations including in high priority areas such as experiential education and technology enhanced learning; teaching evaluations; and letters of reference.

 
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