Staff Research Scientist (Machine Learning Specialist) - SDL6

Job Description

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 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 energySDL2 - Organic small molecules for sustainability and healthSDL3 - Medicinal chemistry for improving small molecule drug candidatesSDL4 - Polymers for materials science and biological applicationsSDL5 - Formulations for pharmaceuticals, consumer products, and coatingsSDL6 - Biocompatibility with organoids / organ-on-a-chipSDL7 - 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

Life science and physical science applications of machine learning in biology, bioengineering or molecular biology or any other relevant fields.Programming and high-performance computingExperience in design of computational pipelines for large-scale imagingExperience with programming languages and scripting methods (i.e. Python, MATLAB, C++, CUDA, Bash, and/or SQL) and machine learning / deep learning methodsActive learning, exploration, optimal experiment design, Bayesian optimization, reinforcement learning, and/or representation learningExperience in development and application of machine learning/deep learning methods for high throughput cell imaging data

Additional expertise that is desired (but not required):

Experience with ML-based tools for image-analysis and signal processing, development of ML prediction toolsExperience with PyTorch and/or TensorFlow, experience with databases and high-content imaging platformsFamiliarity with generative modelingExperience with advanced ML techniques for representation learning and multi-modal data integration

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:

Machine learning for SDL Development

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.

SDL and Automation Development

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.

Research Direction

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:

Managing the research and development projects of AC’s industry partners when implemented in AC labsDeveloping plans supporting research collaborations and estimating financial resources required for programs and/or projectsWorking with Product Managers to ensure research outcomes meet partner requirementsPromoting AC’s research capacity, including delivering presentations at conferencesCollaboration in preparing and submitting research proposals to granting agencies and progress reportingPreparing manuscripts for submission to peer review publications/journals and stewarding them through the processMentor junior lab members and promote a collaborative team environment

Other

Supporting consulting services related to the application of SDLs for materials discovery for the AC’s partnersSupport research-focused events such as Annual Symposium

MINIMUM QUALIFICATIONS:

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

Experience

One (1) to 5 years of experience (inclusive of PhD and/or post-graduate work) in accelerated research and development in the area of development and application of machine learning/deep learning methods for biological or chemical data analysis Experience working closely with a Principal Investigator or as a Principal Investigator or as Project Director with responsibilities of managing, developing and executing a major research project in the area of AI, machine learning, and/or advanced computational analysis and modeling of biological phenomenaExperience with overseeing the activities of a lab.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 excellenceMust have a strong scholarly publication record.

Skills

Skills in electronic/hardware-oriented programming and machine learningStrong and effective communicator in oral and written EnglishCollegial in working with team members and collaborators.Ability to work independently.

Other

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

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

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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.

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 uoft.careers@utoronto.ca.

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