Research Associate

Responsibilities

• Apply machine learning methods to experimental datasets and train models for autonomous materials discovery workflows
• Establish systematic data collection, curation, and management practices across synthesis, processing, and characterization workflows
• Operate, maintain, and improve characterization workflows involving XPS, XRD, SEM, and related tools
• Develop robotic and automated workflows to enable reproducible, high-throughput materials characterization
• Work closely with scientists to advance characterization, data interpretation, and application demonstration testing
• Support the development, integration, and continuous improvement of automated inorganic materials discovery platforms

Requirements

• PhD in Materials Science and Engineering or an equivalent discipline
• Minimum 1 year of research experience in inorganic and soft materials synthesis and characterization
• Proficiency in inorganic and soft materials synthesis and characterization (spectroscopic and electrical)
• Experience in X-ray diffraction (XRD) experimental workflow development and analysis
• Strong project management, leadership, and interpersonal skills to work within a multi-disciplinary international team

About the Company

The Acceleration Consortium (AC) at the University of Toronto is leading a transformative shift in scientific discovery. By leveraging artificial intelligence, robotics, materials sciences, and high-throughput chemistry, the AC creates self-driving laboratories (SDLs) that rapidly design materials and molecules for a sustainable and resilient future.