Staff Scientist (Quantum for AI-Accelerated Materials Discovery)

Date Posted: 11/11/2026

Req ID: 50839

Faculty/Division: Faculty of Arts & Science

Department: Acceleration Consortium

Campus: St. George (Downtown Toronto)

Description:

The Acceleration Consortium is looking for independent 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 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 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:

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 independently to develop research programs that leverage the AC’s SDLs and support the research objectives of AC faculty and industry partners. Including the development of quantum computational tools and machine learning algorithms to predict materials and molecules with desired properties by elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc.

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

Tasks Include:

Managing the research and development projects of AC’s industry partners when implemented in AC labs.

Developing plans to support research collaborations and estimating the 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.

Other:

Supporting consulting services related to the application of SDLs for materials discovery for the AC’s partners.

Support research-focused events such as the Annual Symposium.

Minimum Qualifications:

Education – Ph.D. in Physics, Chemistry, Materials Science, Computer Science, Quantum Information, or a related quantitative field.

Experience

Five (5) to 10 years of experience (inclusive of PhD and/or post-graduate work) in research and development, preferably with significant experience in quantum algorithms.

Experience developing or analyzing quantum algorithms for classical or quantum data.

Strong foundation in quantum mechanics, solid-state physics, or quantum chemistry workflows.

Hands-on experience with atomistic and quantum simulation packages.

Experience with High-Performance Computing (HPC) job scheduling and large-scale data processing.

Experience with Qiskit, scientific Python libraries, machine-learning frameworks, or Git/GitHub.

Experience running experiments on quantum processors or combining quantum measurement data with classical machine learning.

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 in 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: 01/08/2027, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Continuing
Schedule: Full-Time
Pay Scale Group & Hiring Zone: $65,122.00 - $150,000 (salary will be assessed based on skills and experience)

Job Category: Research Administration & Teaching