Postdocs in Materials Science: The University of Toronto Scarborough is seeking a dedicated and innovative researcher for a Postdoctoral Research Position in the field of inorganic cathode materials for Lithium-ion batteries. The successful candidate will contribute to cutting-edge research, focusing on improving performance, stability, and sustainability of inorganic cathode materials.
Position 1: Postdoc – Inorganic Cathode Materials for Lithium-Ion Batteries (Experiments)
| Designation | Research Area | Location | Eligibility/Qualification |
|---|---|---|---|
| Postdoctoral Researcher | Inorganic Cathode Materials for Lithium-Ion Batteries (Experiments) | 1065 Military Trail, Scarborough | Ph.D. in Materials Science, Chemistry, Chemical Engineering, or related field, with a focus on battery materials. |
Job Description:
- Conduct advanced research on inorganic cathode materials, emphasizing performance enhancement.
- Synthesize and characterize new materials using analytical and electrochemical techniques.
- Collaborate with a multidisciplinary team to integrate materials into next-gen Li-ion batteries.
- Analyze data, prepare reports, and present findings in conferences and journals.
How to Apply: Interested candidates should email the following to o.voznyy[at]utoronto.ca by December 15, 2023:
- Detailed CV
- Link to Google Scholar profile
- Contact information for at least two references
Last Date for Apply: December 15, 2023
Location: 1065 Military Trail, Scarborough
Position 2: Postdoc – Machine Learning for Organic and Inorganic Materials Discovery (Theory)
Summary: The University of Toronto Scarborough invites applications for a Postdoctoral Research Position in machine learning for materials discovery. The candidate will play a key role in developing and implementing machine learning algorithms to predict and optimize organic and inorganic material properties.
| Designation | Research Area | Location | Eligibility/Qualification |
|---|---|---|---|
| Postdoctoral Researcher | Machine Learning for Organic and Inorganic Materials Discovery (Theory) | 1065 Military Trail, Scarborough | Ph.D. in Materials Science, Computer Science, Computational Chemistry or Physics, or related field, with a focus on machine learning for materials discovery. |
Job Description:
- Develop and implement machine learning algorithms for material property prediction.
- Collaborate with experimental researchers to validate predictions and synthesize novel materials.
- Process and analyze large datasets, identifying trends and correlations in material properties.
- Contribute to the creation of a materials database and stay updated on advancements in relevant fields.
How to Apply: Interested candidates should email the following to o.voznyy[at]utoronto.ca by December 15, 2023:
- Detailed CV
- Link to Google Scholar profile
- Contact information for at least two references
Last Date for Apply: 15 December 2023
Location: 1065 Military Trail, Scarborough
Join us in our mission to revolutionize energy storage and contribute to a sustainable future! The University of Toronto is committed to equity, diversity, and inclusion. We actively encourage applications from members of groups experiencing barriers to equity. Read our full equity statement here.
Disclaimer: This post is based on information from a reliable source. Candidates are encouraged to verify details on the official University of Toronto website for the most accurate and up-to-date information.
