Research Associate (SDL1, One-year Term)

University of Toronto • Toronto, ON

7d • CA$53,520 - CA$100,350 • Onsite

About The Position

The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. The AC promotes an inclusive research environment and supports the EDI priorities of the unit. The Acceleration Consortium received a $200M Canadian First Research Excellence Grant for seven years to develop self-driving labs for chemistry and materials, the largest ever grant to a Canadian University. This grant will provide the Acceleration Consortium with seven years of funding to execute its vision. This posted position is for a role within the AC’s SDL1 (Inorganic Materials). Supporting and under the guidance of Staff Scientists / Senior Staff Scientist, the Research Associate is expected to carry out and contribute to research projects, by way of their expertise, in the areas below.

Requirements

• PhD in Materials Science and Engineering or equivalent
• Minimum 1 year of experience in conducting research projects in inorganic and soft materials synthesis and characterization
• Inorganic and soft materials synthesis and characterization (spectroscopic and electrical)
• x-ray diffraction experimental workflow develop and analysis
• Project management skills
• Leadership skills
• Interpersonal skills to assist in the guidance and implementation of the research project that is comprised of a multi-disciplinary and international team of scholars.

Responsibilities

• Apply machine learning methods to experimental datasets and train models for autonomous materials discovery workflows.
• Establish systematic data collection, curation, and management practices across synthesis, processing, and characterization workflows.
• Operate, maintain, and improve characterization workflows involving XPS, XRD, SEM, and related tools.
• Develop robotic and automated workflows to enable reproducible, high-throughput materials characterization.
• Work closely with scientists to advance characterization, data interpretation, and application demonstration testing across projects.
• Support the development, integration, and continuous improvement of automated inorganic materials discovery platforms.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree