Staff Scientist (AI for Self-Driving Labs)

Staff Scientist (AI for Self-Driving Labs)

Date Posted: 10/06/2026
Req ID: 50457
Faculty/Division: Faculty of Arts & Science
Department: Acceleration Consortium
Campus: St. George (Downtown Toronto)

Description:

The 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 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 SDLsSDL1 - Inorganic solid-state materials for advanced materials and energySDL2 - Organic small molecules for sustainability and healthSDL3 - Medicinal chemistry for improving small molecule drug candidatesSDL4 - Polymers for materials science and biological applicationsSDL5 - Formulations for pharmaceuticals, consumer products, and coatingsSDL6 - Human organ mimicry with organoids / organ-on-a-chipSDL7 - 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 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 DevelopmentWorking 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.

Research DirectionWorking 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 machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc.

Tasks include:

Managing the research and development projects of AC’s industry partners when implemented in AC labs.Developing plans supporting research collaborations and estimating financial resources required for programs and/or projects.Working with Product Managers to ensure research outcomes meet partner requirements.Promoting AC’s research capacity, including delivering presentations at conferences.Collaboration in preparing and submitting research proposals to granting agencies and progress reporting.Preparing manuscripts for submission to peer review publications/journals and stewarding them through the process.

OtherSupporting consulting services related to the application of SDLs for materials discovery for the AC’s partners.Support research-focused events such as Annual Symposium.

MINIMUM QUALIFICATIONS:

Education – Ph.D. in Computer Science, Software Engineering, Physics, Chemistry, Materials Science, Biology, or equivalent.

Experience:

Five (5) to ten (10) years of experience (inclusive of PhD and/or post-graduate work) in research and development, preferably with significant experience in AI for self-driving labs.Experience in AI for science.Experience in the development of AI tools for self-driving labs.Experience working closely with a Principal Investigator or as a Principal Investigator or as Project Director with responsibilities of managing, developing and executing a major research project in the area of AI and automation, including AI utilization in experimental planning, and workflow establishment for seamless integration of experiments and simulations.Strong experience and expert knowledge of AI and automation.Experience working with industry partners and on industry led research and development projects.Strong experience presenting research at academic conferences.Demonstrated record of academic and/or research excellence.

Skills:

Expert Skills Python, LATEX, Git, Microsoft Office.Strong and effective communicator in oral and written English.Collegial in working with team members and collaborators. Ability to work independently.

Other:

Must have a strong publication record.Demonstrated success in writing and preparing manuscripts, presentations, reports, briefs, and scientific abstracts and manuscripts for peer-reviewed journals.

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

Please refer to our website for some general information about benefits.

Closing Date: 11/21/2026, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Continuing
Schedule: Full-Time
Pay Scale Group & Hiring Zone: $62,617.00 - $150,000 (salary will be assessed based on skills and experience)
Job Category: Research Administration & Teaching

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Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.

As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.

The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.

If you require any accommodations at any point during the application and hiring process, please contact uoft.careers@utoronto.ca.

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