Assistant Professor – AI Materials Discovery

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The Department of Materials Science and Engineering in the Faculty of Applied Science and Engineering at the University of Toronto invites applications for a full-time tenure stream position in AI Materials Discovery. The appointment will be at the rank of Assistant Professor, with an anticipated start date of July 1, 2025.

Candidates must have earned a PhD degree in Materials Science and Engineering, Chemical Engineering, Chemistry, Condensed Matter Physics, Computer Science or a related area by the time of appointment, or shortly thereafter, with a demonstrated record of excellence in research and teaching. Eligibility to register as a Professional Engineering in Ontario is strongly desirable . We seek candidates whose research and teaching interests complement and enhance our existing departmental strengths. The successful candidate will be expected to pursue innovative and independent research, and to establish an outstanding, competitive, and externally funded research program.

The successful candidate will demonstrate innovation and excellence in research and teaching in the area of computational materials science. The candidate should have a strong theoretical background and extensive research experience in computational approaches for the design and discovery of advanced materials. Specific areas of interest include, but are not limited to, materials modeling, atomic-scale simulations, and multiscale approaches to understanding complex material behaviors.

Expertise in AI-accelerated materials discovery, including machine learning, data-driven modeling, and automation of computational workflows, will be highly regarded. Candidates with experience integrating computational techniques with experimental validation are particularly encouraged to apply. While the position emphasizes computational expertise, familiarity with experimental methods in materials research would be considered an asset.

The successful candidate is expected to establish a dynamic research program that leverages computational tools to address challenges in materials design and discovery. Demonstrable experience in industrial applications or collaborative work with industrial partners is also desirable.

In addition, the candidate should be capable of teaching courses in areas such as computational materials science, materials chemistry, and related topics. The ability to mentor students in interdisciplinary projects bridging computational and experimental research will be viewed as a strength.

Candidates must provide evidence of research excellence which can be 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.

Evidence of excellence in teaching will be demonstrated by teaching accomplishments, and the teaching dossier, including a teaching statement, sample course materials, and teaching evaluations or other evidence of superior performance in teaching-related activities submitted as part of the application, as well as strong letters of reference. Other teaching-related activities can include performance as a teaching assistant or course instructor, experience leading successful workshops or seminars, student mentorship, or excellent conference presentations or posters.

Salary will be commensurate with qualifications and experience.

Established in 1827, the University of Toronto is Canada’s largest university, recognized as a global leader in research and teaching. The University’s distinguished faculty, institutional record of ground breaking scholarship and wealth of innovative academic opportunities continually attract outstanding students and academics from around the world. The Department of Materials Science and Engineering at the University of Toronto is home to the top Materials Science and Engineering program in Canada. We foster a world-class environment that excels in teaching, learning and research.

All qualified candidates are invited to apply online by clicking the link below. Applicants must submit a cover letter; a current curriculum vitae; a research statement outlining current and future research interests; a recent writing sample; and a teaching dossier including a teaching statement, sample course materials, and teaching evaluations or evidence of superior performance in other teaching-related activities as listed above.

Applicants must provide the name and contact information of three references. The University of Toronto’s recruiting tool will automatically solicit and collect letters of reference from each referee the day after an application is submitted. Applicants remain responsible for ensuring that referees submit recent letters (on letterhead, dated and signed) by the closing date. More details on the automatic reference letter collection, including timelines, are available in the candidate FAQ.

Submission guidelines can be found at http://uoft.me/how-to-apply. Your CV and cover letter should be uploaded into the dedicated fields. Please combine additional application materials into one or two files in PDF/MS Word format. If you have any questions about this position, please contact Professor Hani Naguib, Chair, Department of Materials Science and Engineering at [email protected] with “AI Materials Discovery” in the subject line.

All application materials, including recent reference letters, must be received by February 16, 2025.

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.

As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

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 [email protected].

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