Staff Scientist (Quantum for AI-Accelerated Materials Discovery)

University of Toronto; Toronto; On-site; CAD 65,000 - 150,000; Full time

Job summary

University of Toronto's Acceleration Consortium seeks Staff Scientists to advance SDLs, applying AI, robotics, and quantum computing to accelerate materials discovery. You will report to the Academic Director and Executive Director and collaborate with world‑leading researchers.

The role emphasizes independent research, cross‑disciplinary teamwork with chemists, materials scientists, and ML researchers, and developing quantum tools to translate lab constraints into predictive models.

Qualifications

• Ph.D. in Physics, Chemistry, Materials Science, Computer Science, Quantum Information, or related field.
• 5–10 years of research and development experience, preferably with quantum algorithms.
• Experience developing or analyzing quantum algorithms for classical or quantum data.
• Strong publication record and ability to communicate results clearly.

Responsibilities

• Develop quantum computational tools and machine learning algorithms for materials discovery.
• Collaborate with AC labs, faculty, and industry partners to align research with SDL goals.
• Prepare manuscripts, grant proposals, and progress reports for dissemination.
• Present research findings at conferences and collaborations.

Skills

Python

LATEX

Git

Microsoft Office

English communication

Independent work

Team collaboration

Education

PhD in Physics, Chemistry, Materials Science, CS, Quantum Information

Tools

Qiskit

Python scientific libraries

Machine learning frameworks

GitHub

Job description

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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 ( https://people.utoronto.ca/employees/ ) 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

Diversity Statement

The University of Toronto embraces Diversity and is building aculture of belonging that increases our capacity to effectivelyaddress and serve the interests of our global community. Westrongly encourage applications from Indigenous Peoples,Black and racialized persons, women, persons withdisabilities, and people of diverse sexual and gender identities.We value applicants who have demonstrated a commitment toequity, diversity and inclusion and recognize that diverseperspectives, experiences, and expertise are essential tostrengthening 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 [e-mail] .

The University of Toronto has implemented a process to substantiate Indigenous Membership/Citizenship/Enrolment for candidates who identify as Indigenous. For more information about this process, please refer to the Indigenous Substantiation Website .