We seek a postdoctoral fellow to help develop an AI-powered self-driving lab to automate multiscale mapping of multicellular, human biological model systems. This fellowship provides a unique opportunity to work at the intersection of biology, engineering, and artificial intelligence, contributing to the future of precision medicine through innovative approaches. The successful candidate will play a key role in advancing machine learning-driven analysis of high-content imaging data and integrating multi-omics datasets within human organ mimicry systems. This work will involve developing novel computational approaches for biological discovery while engaging in a highly interdisciplinary research environment.
This is an Acceleration Consortium Post Doctoral Fellowship supervised by Staff Scientists Dr. Ilya Yakavets and Dr. Yimu Zhao in the Human Organ Mimicry self-driving lab (SDL) with a direct reporting line to co-supervisor Professor Gary Bader in the Departments of Computer Science and Molecular Genetics at the University of Toronto.
The Human Organ Mimicry SDL is an autonomous, AI-driven lab focused on advancing material development, drug discovery, and personalized medicine. By integrating organoids, organ-on-chip technology, biosensors, and machine learning, the lab creates biomimetic models that closely replicate human organ functions and generate clinically relevant data. These models enable medium-throughput, high-content experimentation, accelerating data-driven health research.
The position provides the chance to contribute to cutting-edge research within a vibrant intellectual community at Canada’s leading university. It offers access to state-of-the-art facilities and an outstanding research environment at the Acceleration Consortium within the University of Toronto, working within a world-class team of scientists dedicated to advancing machine learning applications in human organ mimicry, precision medicine, and more. The Department of Computer Science is globally recognized for its pioneering research in artificial intelligence, while the Department of Molecular Genetics is internationally acclaimed as a premier institution for biomedical and life sciences research and education. Furthermore, the Donnelly Centre for Cellular and Biomolecular Research serves as a hub for interdisciplinary collaboration, fostering the integration of functional genomics, computer science, engineering, and biology to address key challenges in biomedical research (www.thedonnellycentre.utoronto.ca).
Situated within the University of Toronto, one of the most concentrated biomedical research communities globally, this position provides access to extensive resources, including fully affiliated academic hospitals and research institutes. The Greater Toronto Area enhances this exceptional academic environment with its cultural and demographic diversity, as well as one of the highest standards of living in the world.
Responsibilities:
Develop advanced machine learning algorithms for analyzing high-resolution microscopy images and extracting biologically meaningful insights.
Create computational tools to study correlations between imaging data and genomic/transcriptomic profiles in human organ mimicry systems.
Implement and optimize cutting-edge techniques such as Bayesian optimization, representation learning, and active learning for experimental design and analysis.
Collaborate with experimentalists, computational scientists, and engineers to align computational methods with experimental workflows.
Prepare and publish high-quality research manuscripts and contribute to grant writing.
Mentor junior lab members and promote a collaborative team environment.
Additional background
The Acceleration Consortium (AC) at the University of Toronto 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, robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories. 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 SDLs 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 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.
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
Experience with advanced ML techniques for representation learning and multi-modal data integration.
Skills and Competencies:
Strong programming and problem-solving skills.
Excellent written and oral communication skills.
Ability to work both independently and collaboratively in an interdisciplinary research environment.
Proven track record of peer-reviewed publications in high-impact journals.
Salary: Competitive and commensurate with qualifications.
Qualifications:
Education: Ph.D. in computational biology, bioinformatics, biomedical engineering, computer science, chemistry, or a related field.
Experience: At least 2 years of experience in one or more of the following areas:
Development and application of machine learning/deep learning methods for biological or chemical data analysis.
Design of computational pipelines for large-scale imaging or genomic data.
Use of high-performance computing environments for computational workflows.
Technical Skills:
Proficiency in Python and experience with other programming and scripting languages such as MATLAB, C++, CUDA, Bash, or SQL.
Familiarity with machine learning libraries and frameworks, such as PyTorch, TensorFlow, and Scikit-learn.
Expertise in active learning, optimal experimental design, Bayesian optimization, and/or reinforcement learning.
Experience with image-analysis tools, signal processing, or the development of ML-based prediction models.
Additional Desired Expertise (Optional):
Knowledge of high-content imaging platforms and databases.
Familiarity with generative modeling
Application Process:
Interested candidates should submit a CV, brief cover letter, and contact information for three references to gary.bader@utoronto.ca referencing SDL6 in the subject. Applications will be reviewed on a rolling basis.