Date Posted: 01/29/2026
Req ID: 46775
Faculty/Division: Faculty of Applied Science & Engineering
Department: Department of Materials Science & Engineering
Campus: St. George (Downtown Toronto)

OVERVIEW:

The Department of Materials Science and Engineering, University of Toronto invites applications for the position of Research Associate (Limited Term) to participate in research on developing generative modeling for inverse design of material characterization and transition states and pathways discovery.

QUALIFICATIONS:

The candidate should hold a PhD in materials science, chemistry, or physics and possess significant expertise in programming with Python and PyTorch and using high performance computing environments. In particular, we are looking for experience in developing machine learning models using graph neural networks and equivariant graph neural networks for materials science applications, as well as experience in generative AI for materials design (e.g., crystal structure generation, transition-pathway generation). The candidate must have open source models published on GitHub or other open source repositories and also papers relevant to the models. The candidate should also have at least 10 years of experience in materials physics and condensed matter and have experience performing DFT calculations using packages such as VASP or Quantum ESPRESSO. Understanding in symmetry of materials, vibrational analysis, and other related properties are required. Finally, it is preferred that the candidate has experience with an understanding of experimental characterization techniques commonly used in materials science, such as x-ray diffraction and Raman spectroscopy.

KEY DUTIES:

• Developing generative models for solving material characterizations such as x-ray diffraction.
• Developing generative models for finding transition states and pathways.
• Developing machine-learning atomic potentials for electrochemistry.
• Developing machine learning models for material-property predictions.
• Designing, planning, and executing research projects.
• Reading published papers relevant to materials science and machine learning developments.
• Coordinating with collaborators, conducting discussion analysis, and reporting of research findings to researchers within and outside of the program.
• Preparing manuscripts for submission to peer reviewed publications/journals.
• Presenting findings at local, national, and international conferences.
• Collaborating efforts with a dynamic and productive team with diverse expertise.

In addition to these duties, the individual will be responsible for contributing to the preparation of grant proposals and for supervising students and research trainees, which will support the lab’s broader research and teaching objectives.

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

Closing Date: 02/27/2026,11:59PM ET
Employee Group: Research Associate 
Personnel Subarea:Research Assoc
Appointment Type: Budget - Term 
Schedule: Full-Time
Pay Scale Group & Hiring Zone: R01 -- Research Associates (Limited Term): $53,520 - $100,350
Job Category: Engineering / Technical