Research Associate

The Acceleration Consortium at the University of Toronto invites applications for a Research Associate (Limited Term) for a 2-year appointment. The anticipated start date is August 1st 2026.

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 Acceleration Consortium (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 Scientists, the Research Associate is expected to carry out and contribute to research projects, by way of their expertise, in the areas below.

• Conduct research in automated and high-throughput materials discovery, including synthesis, processing, characterization, testing, and application-driven evaluation.
• Design and execute high-throughput experimental workflows to investigate structure-property-performance relationships in inorganic materials.
• Develop, implement, and optimize automated research workflows with real-time monitoring, data acquisition, feedback, and reproducibility controls.
• Analyze experimental datasets using computational, statistical, and/or AI/ML methods to extract insights into material performance, stability, and process-structure-property relationships.
• Adapt and improve experimental platforms, testing workflows, and characterization protocols to address specific research questions and improve data quality.
• Designing, modifying, and fabricating custom laboratory apparatus, fixtures, and devices.
• Develop research protocols, data-management practices, technical documentation, and best practices for automated and high-throughput experimentation.
• Contribute to manuscripts, technical reports, presentations, research proposals, and collaborative project planning.

Applicants should apply online at the link below and include a covering letter, curriculum vitae

and three reference names with their contact addresses and phone numbers. Any questions regarding this position should be directed to Sean Caffrey (sean.caffrey@utoronto.ca).

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