Staff Research Scientist (AI for Chemistry, term 2-year)

Date Posted: 04/25/2025
Req ID: 42677
Faculty/Division: Faculty of Arts & Science
Department: Acceleration Consortium
Campus: St. George (Downtown Toronto)

Description:

The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs), also called materials acceleration platforms (MAPs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. AC Staff Research Scientists will advance the field of AI-driven autonomous discovery and develop the materials and molecules required to address society’s largest challenges, such as climate change, water pollution, and future pandemics.

The Acceleration Consortium (AC) promotes an inclusive research environment and supports the EDI priorities of the unit.

Hiring is occurring on a rolling intake. Please apply ASAP and do not wait for the listed job closing date.

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 is developing seven advanced SDLs plus an AI and Automation lab:

• SDL1 - Inorganic solid-state compounds 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 - Biocompatibility with organoids / organ-on-a-chip
• SDL7 - Synthetic scale-up of materials and molecules (University of British Colombia partner lab)
• A central AI and Automation lab to support all the SDLs

This posted position is for a role with SDL2 (Organic Small Molecules)

We seek a scientist to develop ML-driven tools for accelerating organic synthesis and novel material discovery. The role involves leveraging cheminformatics and generative models to design molecules, predict reaction pathways, and optimize materials for applications in sustainability, healthcare, or energy.

Expertise that is desired: Cheminformatics and Machine Learning

• Demonstrated development or application of machine learning tools to address chemical problems, including but not limited to: property prediction, reaction prediction, condition optimization, retrosynthesis, interpretable machine learning, nature language processing for data-mining and human-robot interfacing, and generative models (e.g. GANs, VAE, and diffusion models) for de novo molecular design.
• Proven experience in cheminformatics pipelines using tools such as RDKit, various molecular representation and encoding methods (SMILES, SELFIES, graph-based models), QSAR, virtual screening, or molecular docking/simulations. Knowledge in quantum chemistry calculations and molecule dynamics.

Additional expertise that is desired (but not required): General Programming

• Dataset curation, database management, and data-mining skills
• Computational chemistry pipelines, and physics-informed material discovery

Additional expertise that is desired (but not required): Organic Chemistry

• Basic understanding of organic chemistry
• Collaborative experience with experimental chemists on synthetic organic chemistry or catalyst development projects.

Staff Scientists will work with a diverse team of leading experts at U of T, including Faculty Prof. Alan Aspuru-Guzik, Prof. Sophie Rousseaux, and Acceleration Consortium Staff Scientists Dr. Han Hao, Dr. Xiaoman Guo, Dr. Yang Cao, and Dr. Eric Isbrandt; as well as the broader Acceleration Consortium team.

The Staff Scientists involved in the AC are highly skilled and experienced researchers who will work independently to develop the AI and automation technologies required to build robust and scalable self-driving labs, manage these SDLs, and design and implement research programs (based on the direction of the AC’s scientific leadership team) that leverage the SDL platforms to discover materials and molecules. Moreover, the Staff Scientists will work collectively, sharing knowledge among each other, and with 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:

• 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. Selecting, procurement, and installation of the equipment required for SDLs.

• Research Direction

Develop research programs that leverage the AC’s SDLs and support 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:

• Implementing research and development projects of AC’s industry partners when implemented in AC labs.
• Developing plans that support research collaborations and planning 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 the preparation and submission of research proposals to granting agencies and progress reporting.
• Preparing manuscripts for submission to peer-reviewed 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 chemistry, materials science, life sciences, physics, engineering, robotics, computer science, applied mathematics or related discipline

Experience

• 1 to 5 years of experience (inclusive of PhD and/or post-graduate work) in accelerated R&D in the area of cheminformatics and molecule design.
• Experience working with industry partners and on industry lead research and development projects.
• Expert knowledge of AI and automation and experience with the development of self-driving laboratories
• Experience presenting research at academic conferences.

Skills

• 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 the writing and preparation of 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

Closing Date: 08/31/2025, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Term
Schedule: Full-Time
Pay Scale Group & Hiring Zone: R01 - Research Associates (Limited Term): $53,520 - $100,350 , salary will be assessed based on skills and experience
Job Category: Administrative / Managerial