Date Posted: 11/04/2025
Req : 45516
Faculty/Division: Faculty of Arts & Science
Department: Acceleration Consortium
Campus: St. George (Downtown Toronto)
Description:
Description:
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), also called materials acceleration platforms (MAPs).
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. AC Staff Scientists will advance the field of AI-driven autonomous discovery and develop the materials and molecules required to address society's largest challenges, such as climate change, water pollution, and future pandemics.
The Acceleration Consortium (AC) promotes inclusive research environment and supports the EDI priorities of the unit.
The Acceleration Consortium received a 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.
The AC is developing seven advanced SDLs. These include:
The AC is developing seven advanced SDLs plus an AI and Automation lab:
SDL1 - Inorganic solid-state compounds for advanced materials and energy
SDL2 - Organic small molecules for sustainability and health
SDL3 - Medicinal chemistry for improving small molecule drug candidates
SDL4 - Polymers for materials science and biological applications
SDL5 - Formulations for pharmaceuticals, consumer products, and coatings
SDL6 - Biocompatibility with organoids / organ-on-a-chip
SDL7 - Synthetic scale-up of materials and molecules (University of British Colombia partner lab)
A central AI and Automation lab to support all the SDLs
This posted position is for a Staff Scientist within SDL1:
Inorganic
Expertise in the following areas is desired:
-Design, development, and operation of electrochemistry self-driving labs
-Deep understanding of self-driving workflows for oxygen-evolving electrocatalyst discovery
-Automated and high-throughput synthesis of metal oxide electrocatalysts
-Automated and high-throughput characterization of electrocatalysts (structure, composition, performance)
The Staff Research Scientists will work with a diverse team of leading experts at U of T, including Professors David Sinton, Jason Hattrick-Simpers, Yu Zou, and more.
The Staff Scientists involved in the AC are highly skilled and experienced researchers who will work independently to develop the AI and automation technologies required to build robust and scalable self-driving labs, manage these SDLs, and design and implement research programs (based on the direction of the AC’s scientific leadership team) that leverage the SDL platforms to discover materials and molecules.
Moreover, the Staff Scientists will work collectively, sharing knowledge among each other, faculty, and trainees.
This role will report to the Academic Director and Executive Director of the Acceleration Consortium.
The components and duties of the work can include:
Working with the AC community, including faculty and partners, to determine the required capabilities of the SDLs to be built. Developing SDL plans to meet user requirements and designing novel instruments for automated material synthesis and characterization. Developing customized hardware and Python software packages to build SDLs. Selecting, procurement, and installation of the equipment required for SDLs.
Working independently to develop research programs that leverage the AC’s SDLs and supports the research objectives of AC faculty and industry partners. Using SDLs to synthesize and characterize large quantities of…
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