Postdoctoral Fellow in Machine Learning: The Acceleration Consortium at the University of Toronto is hiring a Postdoctoral Fellow to advance the integration of machine learning with colloidal materials science. This position focuses on developing cutting-edge algorithms, feature representations, and automated platforms for colloidal material synthesis and optimization.
Postdoctoral Fellow in Machine Learning for Colloids and Soft Materials
Designation
Postdoctoral Fellow
Research Area
Machine Learning for Colloids and Soft Materials
Location
University of Toronto, Greater Toronto Area, Canada
Eligibility/Qualifications
Education
- Ph.D. in Chemistry, Chemical Engineering, Pharmaceutical Sciences, or related STEM fields.
Required Experience
- Minimum of 2 years in applying machine learning to science, with expertise in explainable AI and/or active learning.
- At least 2 years of hands-on experience in synthesizing and characterizing colloids such as emulsions, micelles, and suspensions.
- Demonstrated success in preparing and presenting scientific findings to both academic and industry audiences.
- Strong publication record in top-tier journals.
Desired Experience
- Familiarity with high-throughput screening and automated laboratory synthesis platforms.
- Publications in prominent machine learning forums (e.g., NeurIPS, ICML, ICLR).
Technical Skills
- Proficiency in Python and additional programming languages (SQL, MATLAB, C++, etc.).
- Knowledge of machine learning libraries such as PyTorch, TensorFlow, and Scikit-learn.
- Exposure to active learning/reinforcement learning.
Job Description
The selected candidate will:
- Develop explainable machine learning algorithms for analyzing and optimizing colloidal systems, particularly emulsions and nanoemulsions.
- Create adaptive experimentation algorithms for automated synthesis.
- Design computational tools and visualizations for feature-target exploration.
- Collaborate with experimentalists and engineers to develop a colloidal self-driving lab.
- Prepare and publish high-quality research manuscripts.
- Present findings to supervisors, stakeholders, and collaborators.
- Mentor junior lab members and foster a collaborative environment.
How to Apply
Interested candidates should prepare and submit:
- A cover letter detailing their experience and qualifications.
- A detailed curriculum vitae (CV).
- Copies of recent publications.
- Contact information for three references.
Applications should be submitted via the University of Toronto’s online application system.
Last Date to Apply
Applications are being accepted immediately and will remain open until the position is filled. Early applications are encouraged.
For more information about the Acceleration Consortium, visit: University of Toronto Acceleration Consortium
