University of Toronto, Statistical Sciences

Position ID:
UofT-Statistical Sciences-COMPSTATS [#25858]
Position Title: 
Assistant Professor, Computational Statistics
Position Type:
Tenured/Tenure-track faculty
Position Location:
Toronto, Ontario M5G 1X6, Canada
Subject Area: 
Statistics
Appl Deadline:
2023/11/20 11:59PMhelp popup* finished (2023/09/21, finished 2024/04/03, listed until 2024/03/21)
Position Description:
   

*** this position has been closed and new applications are no longer accepted. ***

Position Description

The Department of Statistical Sciences in the Faculty of Arts and Science at the University of Toronto invites applications for a full-time tenure stream position in the area of Computational Statistics. The appointment will be at the rank of Assistant Professor with an anticipated start date of July 1, 2024.

 

This search aligns with the University’s commitment to strategically and proactively promote diversity among our community members (Statement on Equity, Diversity & Excellence). Recognizing that Black, Indigenous, and other Racialized communities have experienced inequities that have developed historically and are ongoing, we strongly welcome and encourage candidates from those communities to apply.

 

Candidates must have earned a PhD degree in Statistics or a related area by the time of appointment, or shortly thereafter, with a demonstrated record of excellence in research and teaching. Experience working with, teaching, or mentoring diverse groups or diverse students is preferred. We seek exceptional candidates whose research and teaching interests complement and strengthen our existing departmental research strengths. The successful candidate will be expected to establish innovative and independent research at the highest international level and to establish an outstanding, competitive, and externally funded research program.

 

Candidates must provide evidence of research excellence as demonstrated by a record of publications in top-ranked and field relevant journals or forthcoming publications meeting high international standards, the submitted research statement, presentations at significant conferences, awards and accolades, and strong endorsements from referees of high standing.

 

Evidence of excellence in teaching will be provided through teaching accomplishments, the teaching dossier including a strong teaching statement, sample course materials, and teaching evaluations, as well as strong letters of reference.

 

Candidates are also expected to show evidence of a commitment to equity, diversity, inclusion, and the promotion of a respectful and collegial learning and working environment demonstrated through the application materials.

 

Salary will be commensurate with qualifications and experience.

 

All qualified candidates are invited to apply online at Academic Jobs Online and must submit a cover letter; a current curriculum vitae; a research statement outlining current and future research interests; a recent writing sample (of no more than 15 pages); and a teaching dossier to include a


teaching statement, sample course materials, and teaching evaluations. Equity and diversity are essential to academic excellence. We seek candidates who value diversity and whose research, teaching and service bear out our commitment to equity.

Candidates are therefore also asked to submit a 12 page statement of contributions to equity and diversity, which might cover 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.

 

Applicants must also arrange to have three letters of reference (dated, on letterhead and signed) uploaded through Academic Jobs Online directly by the writers by the closing date.

 

All applicant materials, including signed reference letters, must be received by November 20, 2023.

 

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

 

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.

 

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.


Application Materials Required:
Submit the following items online at this website to complete your application:
  • Cover letter
  • Curriculum Vitae
  • Research statement
  • Teaching statement
  • Publication list
  • Teaching Evaluations
  • Sample Syllabi
  • Course Materials
  • Equity and Diversity Statement
  • Recent Publication
  • Three reference letters (to be submitted online by the reference writers on this site help popup)
And anything else requested in the position description.

Further Info:
www.statistics.utoronto.ca
 
700 University Avenue
Suite 9005
Toronto, ON M5G 1X6