Assistant Professor, Statistical Methods for Environmental Sciences
The Department of Statistical Sciences and the School of the Environment in the Faculty of Arts and Science at the University of Toronto invite applications for a full-time (51% Statistical Sciences, 49% School of the Environment) tenure stream position in the area of statistical methods for environmental science. The appointment will be at the rank of Assistant Professor and will commence on July 1, 2020 or shortly thereafter.
We are interested in candidates who demonstrate excellence in teaching and research, with research interests in any area of data science related to climate and global environmental change, particularly environmental time series analysis; design and analysis of large-scale environmental monitoring data; design and interpolation with computer experiments; uncertainty quantification of complex mathematical models; decision analysis; estimation of causal relationships; network analysis; extreme value estimation; and remote sensing; and whose research and teaching interests complement and strengthen our existing departmental strengths in Statistical Sciences and The School of the Environment.
Applicants must have earned a PhD degree in Statistics, Quantitative Environmental Science or a related area by the time of appointment, or shortly thereafter. Excellence in research is evidenced primarily by the quality of published papers in top ranked and field-relevant academic journals or forthcoming publications meeting high international standards, the submitted research statement, presentations at conferences, research awards and accolades, and strong endorsements by referees of top international stature. Evidence for excellence in teaching is based on strong letters of reference, teaching accomplishments and a teaching dossier containing a strong statement of teaching philosophy, teaching evaluations and sample syllabi.
The University of Toronto is an international leader in psychology and statistical science research and education. 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. 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 will be commensurate with qualifications and experience.
Applicants are to apply online at AcademicJobsOnline by using the following link https://academicjobsonline.org/ajo/jobs/14919 and submit a cover letter; a current curriculum vitae; a list of publications or forthcoming publications; a research statement outlining current and future research interests; one sample article-length publication; and a teaching dossier to include a teaching statement, sample course syllabi, and teaching evaluations. Applicants must also arrange to have at least three letters of reference (on letterhead and signed) uploaded through AcademicJobsOnline directly by the writers. All application materials, including signed reference letters, must be received by November 25, 2019.
For more information about the Department of Statistical Sciences or the School of the Environment, please visit our websites at https://www.statistics.utoronto.ca and https://www.environment.utoronto.ca or contact Katrina Mintis, Department of Statistical Sciences at email@example.com and Stella Kyriakakis, School of the Environment at firstname.lastname@example.org.
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.