Staff Research Scientist (AI for Self-Driving Labs, 2-year Term)

University of Toronto•Toronto, ON

35d

About The Position

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. 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. These include: SDL0 - A central AI and Automation lab to support all the SDLs 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 partnerlab) This posted position is for a role within SDL0: AI & Automation Experience in one or more of the following is required: Agentic and sequential decision-making for autonomous experimentation, including activelearning and optimal experimental design Generative and probabilistic modeling, including uncertainty estimation, risk-aware prediction,and data-efficient learning Continual, transfer, and meta-learning, with emphasis on sim-to-real and real-to-simgeneralization Applied machine learning on real-world experimental or industrial data, including multivariatetime-series and noisy, sparse, or incomplete datasets Close collaboration with experimental scientists, translating scientific objectives into ML-drivenor autonomous systems The Staff Research Scientists will work with a diverse team of leading experts at U of T, including:Professors Anatole von Lilienfeld, Florian Shkurti, Animesh Garg, Alán Aspuru-Guzik, Oleksandr Voznyy, and more. The Staff Research Scientists involved in the AC are highly skilled and experienced researchers whowill work independently to develop the AI and automation technologies required to build robust andscalable 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 todiscover materials and molecules. Moreover, the Staff Research 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.

Requirements

Ph.D. in Computational Chemistry or equivalent
1 to 5 years of experience (inclusive of PhD and/or post-graduate work) in research and development, preferably with significant experience in computational chemistry and self-drivinglab orchestration
Experience in computational chemistry (property prediction and validation)
Experience in development of self-driving lab orchestration tools and their implementation
Experience working closely with a Principal Investigator or as a Principal Investigator or as Project Director with responsibilities of managing, developing and executing a majorresearchproject in the area of AI and automation, including hardware integration forautomation, high throughput experimentation for dataset generation, AI utilization inexperimental planning, and workflow establishment for seamless integration of experimentsand simulations
Strong experience and expert knowledge of AI and automation
Experience working with industry partners and on industry led research and developmentprojects
Strong experience presenting research at academic conferences
Demonstrated record of academic and/or research excellence
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
Must have a strong publication record
Demonstrated success in writing and preparing manuscripts, presentations, reports, briefs, andscientific abstracts and manuscripts for peer-reviewed journals
Experience in one or more of the following is required:
Agentic and sequential decision-making for autonomous experimentation, including activelearning and optimal experimental design
Generative and probabilistic modeling, including uncertainty estimation, risk-aware prediction,and data-efficient learning
Continual, transfer, and meta-learning, with emphasis on sim-to-real and real-to-simgeneralization
Applied machine learning on real-world experimental or industrial data, including multivariatetime-series and noisy, sparse, or incomplete datasets
Close collaboration with experimental scientists, translating scientific objectives into ML-drivenor autonomous systems

Responsibilities

Working with the AC community, including faculty and partners to determine the requiredcapabilities of the SDLs to be built.
Developing the plans for SDLs that will meet user requirementsand designing novel instruments for automated material synthesis and characterization.
Developing customized hardware and Python software packages to build SDLs.
Selection, procurement, andinstallation of the equipment required for SDLs.
Working independently to develop research programs that leverage the AC’s SDLs and supportstheresearch objectives of AC faculty and industry partners.
Using SDLs to synthesize and characterizelarge quantities of candidate molecules, calibrating theoretical models with experimental data,predicting promising candidates with computational tools and machine learning algorithms, andelucidating structure-property relationships of emerging molecules, polymers, solid-state materials,formulations, etc.
Managing the research and development projects of AC’s industry partners when implementedin AC labs
Developing plans supporting research collaborations and estimating financial resourcesrequired 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 andprogress reporting
Preparing manuscripts for submission to peer review publications/journals and stewarding them through the process
Supporting consulting services related to the application of SDLs for materials discovery for the AC’s partners
Supporting research-focused events such as the Annual Symposium