Our group (CLEAN@NRC) seeks a Postdoctoral Fellow for projects applying machine learning to experimental and first principals databases for the prediction and development of new materials.
Work will take place in two domains: metal oxide catalysists and metal composities.
The PDF will work with both with public data (avail from sources such as The Materials Project, Citrine, etc) and experiments conducted over the course of the projects to extract features and descriptors for chemical and physical performance.
In both domains, the goal is to enable Automated Material Discovery by developing machine learning models which can both predict performance and suggest new compositions and processing steps to improve it. This will be accomplished through the application of both existing AI/ML methods and the development of new ones.
The ideal candidate will have extensive experience using machine learning and deep learning in the domain of chemistry, materials, or engineering. Candiates with significant experience in either AI/ML OR computational approaches to electrocatalysis / alloy design are also highly encouraged to apply.
Necessary software development experience:
Nice to have development experience:
Compensation => competitive salary and benefits for two years (with the possiblity of extension based on performance) + conference travel allowance
Location(s) => The position will begin in Ottawa, Ontario, Canada. During the 2nd year, it will involve a move to NRC's newest site in Missaugua (moving costs will be paid for by NRC).
Applicants should provide a CV and a cover letter describing how they meet the criteria to: isaac.tamblyn@nrc.ca
Deadline => Applications will be reviewed as they are recieved
For more information about our group and research interests, please see: our group