This role will lead the planning, development and execution of training programs for students, faculty, and external partners. Training areas will include applied machine learning, robotics, laboratory automation, and materials discovery, which will be delivered across numerous platforms, such as micro-credential programs, academic courses, hands-on workshops, lectures, and public outreach.
As a Software Engineer you will play a pivotal role in managing and optimizing the AC's software infrastructure to support high-impact research projects in accelerated material discovery.
This postdoctoral fellow will help develop machine learning frameworks and feature representations for colloidal materials, while supporting the development of automated platforms for data collection and synthesis.
Postdoctoral fellows will contribute to the AC's self-driving lab for inorganic material synthesis and characterization, developing automated platforms for electrochemical material discovery and advancing CO₂ conversion and green hydrogen technologies.
This Staff Scientist (5–10 years of experience) will work in the AC's self-driving lab for polymers for materials science and biological applications.
This Staff Research Scientist (<3 years of experience) will work in the AC's self-driving lab for polymers for materials science and biological applications.
This postdoctoral fellow will advance AI-powered self-driving labs for automating multiscale mapping of human biological models, integrating machine learning and multi-omics to drive innovation in precision medicine.
This Senior Staff Scientist (10+ years of experience) will help lead the AC's self-driving lab for polymers for materials science and biological applications and oversee other staff in the lab.
Train to become the next generation of scientist. Apply to be an Acceleration Consortium RA, PDF, or graduate student at the University of Toronto or the University of British Columbia.