Staff Research Scientist (Machine Learning Specialist) - SDL6

Staff Research Scientist (Machine Learning Specialist) - SDL6

Date Posted : 09 / 02 / 2025

Req ID : 45033

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.

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 AC 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 Columbia partner lab)
• A central AI and Automation lab to support all the SDLs

Position Overview :

We are seeking a motivated and skilled researcher to join the Acceleration Consortium working with the Human Organ Mimicry SDL. The Self-Driving Lab (SDL) focused on Human Organ Mimicry (HOM) embodies an autonomous artificial intelligence (AI)-assisted platform for culturing and screening high-fidelity models of functional tissues and diseases. In addition to fundamental capabilities like cell passaging and sample preparation, the platform will facilitate closed-loop optimization campaigns designed to optimize cell culture conditions (e.g., growth media and extracellular matrix support), automated generation of model-specific datasets and production of highly reproducible batches of cells with specific phenotypes (e.g., patient-derived organoids (PDOs) and differentiated iPSCs), and development of advanced automated workflows and AI tools (e.g., static and dynamic co-culture organ-on-a-chip (OOC) models, colony picking and bioprinting).

The ideal candidate should have strong expertise performing machine learning (ML), computational biology with the capability and / or experience to apply those skills toward imaging data (e.g., live cell microscopy). The successful candidate will contribute to advancing machine learning-driven analysis of high-content imaging data to achieve 1) better OOC tissue model functional evaluation and clinical benchmarking, 2) optimization on cost-efficient workflow and reproducibility. The candidate must have knowledge of current machine learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred. Strong coding skills in Python or a comparable programming language are expected, with the ability to develop analysis pipelines and tools that meet project deadlines and are suitable for publication-quality research. The role will involve developing novel computational approaches for biological discovery and working collaboratively in an interdisciplinary research environment. Additional expertise related to biological knowledge of the wet lab experimentation required to gather imaging data is an optional benefit.

This posted position is for a Staff Research Scientist at SDL6 (Human Organ Mimicry).

This is a two-year term with the possibility of renewal.

Expertise that is desired :

Computational expertise

Additional expertise that is desired (but not required) :

The Staff Research Scientist will work with a diverse team of leading experts at U of T, including Professors Alán Aspuru-Guzik, Anatole von Lilienfeld, Florian Shkurti, Animesh Garg, Oleksandr Voznyy, Robert Batey, Cheryl Arrowsmith, Milica Radisic, and Vuk Stambolic. The Staff Research Scientist will also work with Staff Scientists in Human Organ Mimicry and AI SDL.

The Staff Research 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 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.

The components and duties of the work can include :

Working with the AC community, including faculty and partners, this candidate will align computational methods with experimental workflows. The focus will be on developing advanced machine learning algorithms for monitoring various in vitro cell culture models (2D, 3D, organoids and OOCs), as well as enabling data-driven autonomous experimentation. Developing computational tools for the analysis of high-resolution microscopy images of complex tissue models and extracting biologically meaningful insights to support quality control and autonomous decision-making.

Working with the AC community, including faculty and partners, determine the required capabilities of the SDLs to be built. Design and testing of closed-loop optimization campaigns for cell culture media optimization using the selected framework. Developing SDL plans to meet user requirements and designing novel instruments for autonomous cell culture experiments. Developing customized hardware and Python software packages to build SDLs. Selecting, procurement, and installation of the equipment required for SDLs.

Working independently to develop research programs that leverage the AC’s SDLs and supports the research objectives of academic and industrial partners. Translating computational approaches ranging from 2D cell culture models to more complex 3D systems (i.e. organoids and OOCs), with the goal of creating advanced biomimetic models that closely replicate human organ functions and produce clinically relevant data. Preparing and publishing high-quality research manuscripts and contributing to grant writing efforts.

Tasks include :

Other

MINIMUM QUALIFICATIONS :

Education – Ph.D. in computational biology, bioinformatics, biophysics, biomedical engineering, computer science, or a related field.

Experience

Skills

Other

All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority

Closing Date : 10 / 31 / 2025, 11 : 59PM ET

Employee Group : Research Associate

Appointment Type : Grant - Term (2 years term with possibility of renewal)

Schedule : Full-Time

Pay Scale Group & Hiring Zone : R01 Research Associates (Limited Term) : $53,520 - $100,350 (salary will be assessed and may go above the range based on skills and experience)

Job Category : Research Administration & Teaching

Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.

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

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