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Job ID: 133007

Associate Professor or Professor - Regional Director of the Canadian Statistical Sciences Institute
University of Toronto


Date Posted Nov. 5, 2019
Title Associate Professor or Professor - Regional Director of the Canadian Statistical Sciences Institute
University University of Toronto
Toronto, ON, Canada
Department Statistical Sciences
Application Deadline Jan. 8, 2020
Position Start Date Jul. 1, 2020
 
 
  • Associate Professor
    Professor
  • Statistics & Actuarial Science
 
 

The Department of Statistical Sciences in the Faculty of Arts and Science at the University of Toronto (UofT) invites applications for a full-time tenure stream position in Statistics. The appointment will be at the rank of Associate Professor or Professor and will commence on July 1, 2020 or shortly thereafter.

We are interested in candidates that demonstrate an exceptional record of excellence in teaching and research whose research and teaching interests complement and strengthen our existing departmental strengths https://www.statistics.utoronto.ca/research. Candidates will have an established international reputation and will be expected to sustain and lead innovative and independent research at the highest international; level and to maintain an outstanding, competitive and externally funded research program.

The successful candidate is expected to lead the Canadian Statistical Sciences Institute of Ontario (CANSSI-Ontario) at UofT, an extra-departmental unit in the Faculty of Arts and Science, administered by the Department of Statistical Sciences, and to serve as the Regional Director of CANSSI Ontario, a regional node of the Canadian Statistical Sciences Institute for an initial term of five years. CANSSI Ontario has been established to provide province-wide leadership in developing scientific collaborations and training programs, and to strengthen the network of statistical scientists across Canada that forms CANSSI. For more information about CANSSI please see http://www.canssi.ca/ .

Applicants must have a PhD degree in Statistics, Biostatistics or a related field and a demonstrated outstanding record in research and teaching. Excellence in research is evidenced primarily by a mature research program with a sustained record of research contributions at the highest international levels expected of a leading researcher, as judged by sustained high impact publications in top ranked and field-relevant academic journals, the submitted research statement, invited presentations at top international conferences, distinguished research awards and accolades, and other noteworthy activities that contribute to the visibility and prominence of the discipline as well as exceptional endorsements by referees of the highest international stature. Evidence for excellence in teaching is based on strong letters of reference, and a teaching dossier containing a strong statement of teaching philosophy, sample syllabi and course materials, teaching accomplishments and accolades and teaching evaluations.

In addition to a record of research and teaching excellence, the successful candidate will possess and demonstrate outstanding leadership, administrative management, communication, and relationship-building skills clearly demonstrated through the application materials. As CANSSI-Ontario Director, the successful applicant is expected to foster provincial, national and international collaborations and partnerships, secure external funding, and build a network of collaborative projects with cognate departments with the ultimate goal of strengthening research and education in statistical science in Ontario.

The University of Toronto is an international leader in statistical science research and education and the successful candidate will join a vibrant intellectual community of world-class scholars at Canada’s leading university. The Greater Toronto Area offers amazing cultural and demographic diversity and one of the highest standards of living in the world.

Salary and rank will be commensurate with qualifications and experience.

Applicants are to apply online at AcademicJobsOnline by using the following link https://academicjobsonline.org/ajo/jobs/15331 and submit a cover letter; a current curriculum vitae; a list of publications; a research statement outlining current and future research interests; up to three article-length sample publications; and a teaching dossier to include a teaching statement, sample course syllabi, and teaching evaluations. Leadership and administrative criteria will be demonstrated with a statement outlining leadership philosophy, a vision for development of CANSSI-Ontario in the next 5 years, and a summary of past achievements in leadership and administrative duties. Applicants must also arrange to have at least three letters of reference (on letterhead and signed) uploaded through AcademicJobsOnline directly by the writers.

All applicant materials, including signed reference letters, must be received by January 8, 2020.

For more information about the Department of Statistical Sciences please visit our websites at https://www.statistics.utoronto.ca or contact Katrina Mintis, Department of Statistical Sciences at katrina.mintis@utoronto.ca.

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.

All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.


  • 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.

    All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.


 
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