Senior Staff Scientist (Quantum for AI-Accelerated Materials Discovery)

at University of Toronto, Toronto, Canada

Listed on EduAtlas: live school vacancies from schools’ own careers pages and public job services. Apply on jobs.utoronto.ca.

Salary

Not given in the advert

Closing date

9 January 2027

Start

Not given in the advert

First seen

11 November 2026, on a public job listing

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The advert

Date Posted: 11/11/2026

Req ID: 50840

Faculty/Division: Faculty of Arts & Science

Department: Acceleration Consortium

Campus: St. George (Downtown Toronto)

Description:

The Acceleration Consortium is looking for independent senior staff scientists to advance the use of quantum algorithms such as Quantum Phase Estimation (QPE), Variational Quantum Eigensolver (VQE), Quantum Adiabatic Evolution & Optimization, and Quantum Machine Learning Algorithms (QML) to accelerate the discovery of new materials and molecules. This scientist will work with a team of scientists with expertise in the use of AI and automation for materials discovery and a team of software engineers.

Experience in one or more of the following is desired:

• Quantum Machine Learning (QML): Research and deploy hybrid quantum-classical AI models, geometric quantum machine learning, and quantum neural networks (QNNs) to accelerate materials property predictions, such as material properties, energy states, and crystal structures, bypassing heavy computational bottlenecks.

• Quantum Algorithm Exploration: Evaluate quantum or hybrid quantum-classical algorithms for molecular and many-body simulation, analyzing where quantum resources provide a distinct scaling advantage over classical methods.

• Software Engineering: Implement algorithms in quantum software frameworks, building modular APIs that connect quantum backends to classical chemistry pipelines.

• Developing Hybrid Workflows: Design and implement integrated computational workflows combining classical machine learning, AI surrogates (like machine-learned force fields), and quantum chemistry or first-principles calculations (DFT, molecular dynamics).

• Cross-Functional Collaboration: Work alongside experimental chemists, materials scientists, and core ML researchers to validate computational predictions and translate laboratory constraints into predictive models.

About the Acceleration Consortium:

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 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 position will work in the AC’s AI and automation lab and with a diverse team of leading experts at U of T, including Faculty and Staff Scientists such as: Alán Aspuru-Guzik, Kourosh Darvish, Florian Shkurti, Chris Sutton, Willi Gottstein, and more.

The Senior Staff Scientists involved in the AC 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 SDL platforms to discover materials and molecules. Moreover, the Senior 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:

1. Quantum simulation and quantum AI algorithm tool development

Working with the AC community, including faculty and partners, to determine how quantum or quantum-inspired tools can increase the capabilities of the AC’s SDLs or materials discovery campaigns.

Working with the AC software engineering team to create software packages to deploy quantum computational tools.

2. Facility Management and Operations

Working with the department of Chemistry, Facilities and Operations, and AC research staff to ensure that the AC labs are fully operational.

• Overseeing the activity of the laboratory technicians servicing the labs

• Supporting the selection, procurement, and installation of equipment Managing laboratory budgets

• Scheduling and coordinating lab access

3. Research Direction

Working as a co-investigator, in partnership with the Principal Investigator, independent management, development, and execution of 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.

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