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Computational discovery of new energy material: ... (No replies)
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Are you looking to work at the forefront of energy materials discovery? Successful candidates will have the opportunity to work with two world-leading theorists and an experimentalist at three outstanding institutions. They will solve real-world problems and simultaneously deepen their computational expertise. We are looking for driven, innovative researchers to work on projects that lever leadership in deep learning and outstanding supercomputer resources to discover new materials for renewable energy (storage and generation).
Position details:
- The candidates will work collaboratively among groups at the NRC, the University of Toronto, and Carnegie Mellon University. This is an opportunity to combine expertise, methods, and datasets.
- Based on expertise, positions available, and geographic considerations, the candidates will hold a full-time appointment at one of the three institutions (NRC, UT, CMU), and will engage regularly online and in-person in addition.
Ideally, candidates will have:
- Expertise in computational chemistry and physics – such as:
o The application of quantum chemistry methods to investigate reaction mechanisms and structure-property relations in electrocatalysis or thermocatalysis;
o And/or the application of quantum chemistry and band structure methods to calculate crystal and electronic structures of semiconductor materials;
- Expertise in high-throughput DFT: familiarity with developing, running, and analyzing calculations to explore wide chemical spaces and have significant experience in python and working in a linux development environment;
- Strong written and oral communication skills, time management skills, and desire to work in research teams.
Additional skills:
- Experience with code sharing and documentation for collaboration projects. Ideally with code samples or published projects on resources like Github, Bitbucket, etc.
- Familiarity with machine learning techniques such as traditional feature-based machine learning, graph convolution methods, deep learning, image recognition, generative models, reinforcement learning, NLP. Comfort with at least one machine learning framework (pytorch, tensorflow, keras, etc).
- Interest in collaborating with an experimental team to apply insights obtained using ML + DFT to the discovery and validation of new catalysts and new semiconductors.
Please apply with a cover letter, your CV, and the names of three referees. Application packages should be emailed to each of:
Ted Sargent- [email protected]
Isaac Tamblyn – [email protected]
Andrew Johnston - [email protected]
Zachary Ulissi – [email protected]