The Department of Computer Science at the University of Regina invites applications for a tenure-track Assistant Professor in Data Science, Machine Learning, or Artificial Intelligence, starting July 1, 2024.

The successful candidate will join a vibrant department of highly motivated researchers and educators including several world-renowned experts and a group of recently recruited early career faculty members who are already making impacts in their respective fields.

We are seeking a candidate who has an exceptional early-career research record in Data Science, Machine Learning, or Artificial Intelligence. In particular, the successful candidate’s record will contain publications in leading international venues in their specific field. The successful candidate will initiate an independent research program, secure external funding to support this research, and recruit and supervise graduate students conducting research in their area of expertise. In their application, candidates are to provide a research plan that shows potential for future publications in leading international venues and for long-term external funding from NSERC; potential for funding through industry partnerships will be considered an additional asset.

The University of Regina main campus is situated on Treaty 4 territory, which is the traditional territory of the Anihšinābēk, Nêhiyawak, Dakota, Lakota, and Nakota Peoples, and the original home of the Métis/Michif Nation.

More information on the Department of Computer Science at the University of Regina can be found at https://www.uregina.ca/science/cs/index.html.

Candidates are expected to hold a Ph.D. in Computer Science or a related field by the time of appointment and be committed to excellence in the three pillars of academia: research, teaching, and service, which will be used as the criteria to evaluate and rank the applications. Details on these criteria are provided below.

Candidates are to provide a teaching philosophy and evidence of pedagogical excellence or potential. The successful candidate will demonstrate their ability to teach core computer science courses in our undergraduate program, as well as courses at the undergraduate and graduate levels aligned with their primary research area. We are particularly interested in candidates who are able to teach courses within the curriculum of our professionally focused Master’s degree in Data Science (https://www.uregina.ca/science/cs/graduate/MSc-Data-Science.html).

Candidates are to demonstrate collegiality and commitment to academic service through committee work, peer review activities, and the support of other academic work (e.g., conference or workshop organization, conducting academic skills seminars). The successful candidate will be a willing participant in academic service, both within the Department/University and within the broader academic community.

Full time.

If you have any questions about this position, feel free to contact the Chair of the Search Committee, Dr. Orland Hoeber (cs.head@uregina.ca).

Applications must be submitted online at https://urcareers.uregina.ca by October 2, 2023, and should include:
-a cover letter
-a curriculum vitae

The following documents (uploaded as ‘other’ in the online application portal)
-names and contact details of three references
-a description of the applicant’s most significant research contributions
-a statement of research plans, and
-a statement of teaching philosophy that includes the applicant’s prior teaching experiences and future teaching plans/interests.

In addition, applicants must arrange for three letters of reference to be sent by email to the chair of the search committee (Dr. Orland Hoeber, cs.head@uregina.ca) ideally by October 2, 2023, but no later than October 16, 2023.

All qualified candidates are encouraged to apply; however, Canadian citizens and permanent residents of Canada will be given priority.

The University of Regina is committed to an inclusive workplace that reflects the richness of the community that we serve. The University welcomes applications from all qualified individuals, including individuals within the University’s employment equity categories of women, persons with disabilities, members of visible minorities, Indigenous persons, individuals of diverse gender and sexual orientation and all groups protected by the Human Rights Code.