Research Scientist; AI Self-Driving Labs, Term

Position: Staff Research Scientist (AI for Self-Driving Labs, 2-year Term)
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Staff Research Scientist (AI for Self-Driving Labs, 2-year Term)   Faculty/Division:  Faculty of Arts & Science
Department:  Acceleration Consortium
Campus:  St. George (Downtown Toronto)

Description:

T he Acceleration Consortium (AC), based at the University of Toronto (U of T), is a global community of academia, industry, and government working to accelerate the discovery and development of advanced materials and molecules. The AC develops self-driving laboratories (SDLs), autonomous research platforms that integrate artificial intelligence, robotics, automation, and advanced computing to dramatically reduce the time and cost of scientific discovery.
AC Staff Research Scientists are highly skilled, experienced, and independent researchers that develop the AI and automation technologies required to build robust and scalable self-driving labs, manage these SDLs, and design and implement collaborative research programs that leverage the SDLs to accelerate discovery. Staff Scientists will advance SDL technologies and apply them to challenges in areas such as clean energy, sustainability, healthcare, and advanced manufacturing.
The Acceleration Consortium (AC) promotes 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.
The AC operates and continuously develops seven SDLs as core facilities:
SDL0 - A central AI and automation lab to advance the robotics and AI tools used in SDLs
SDL1 - Inorganic solid-state materials 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 - Human organ mimicry with organoids / organ-on-a-chip
SDL7 - Synthetic scale-up of materials and molecules (University of British Colombia partner lab)
This posted position is for a role within SDL0: AI & Automation.
Experience in one or more of the following is desired:
Close collaboration with experimental scientists, achieving scientific objectives with AI-driven systems.
Agentic and sequential decision-making for autonomous experimentation, including active learning and optimal experimental design.
Generative and probabilistic modeling, including uncertainty estimation, risk-aware prediction, and data-efficient learning.
Applied machine learning on real-world experimental or industrial data, including multivariate time-series and noisy, sparse, or incomplete datasets.
Orchestration and control of self-driving laboratories, including experimental workflow automation, instrument integration, and real-time data processing.
Staff Research Scientists will work with a diverse team of leading experts at U of T, including Faculty and Staff Scientists in and associated with SDL0 such as:
Alán Aspuru-Guzik, Anatole von Lilienfeld, Kourosh Darvish, Florian Shkurti, Chris Sutton, Willi Gottstein, and more. Moreover, the Staff Scientists will work collectively, sharing knowledge among each other (spanning all AC labs), local and global AC faculty, and the many trainees that work in these labs.
This role will report to the Academic Director and Executive Director of the Acceleration Consortium.
The components and duties of the work can include:
SDL and Automation Development
Working with the AC community, including faculty and partners to determine the required capabilities of the SDLs to be built. Developing the plans for SDLs that will meet user requirements and designing novel instruments for automated material synthesis and characterization. Developing customized hardware and Python software packages to build SDLs. Selection, procurement, and installation of the equipment required for SDLs.
Research Direction
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 candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and…