New post from InsilicoMatters
PhD in Computational Materials Design For Energy Applications (Machine Learning) in Quebec Canada
Project title: Designing efficient and affordable energy materials via computational modeling (Machine Learning)
- Institut National de la Recherche Scientifique (INRS) is dedicated exclusively to graduate-level research and training covering four main areas of research: Energy, Materials, and Telecommunications; Social Sciences; Environment and Geosciences; and Health and Biotechnology. Since its creation in 1969, the institute has built its success on interdisciplinarity, innovation, and excellence. In Québec, it has ranked first for twelve years now in terms of research intensity. In Canada, it has risen from second to first place for the year 2018-2019, with the highest funding received per faculty member and student member.
- Project description: The successful applicant will work on an exciting research program that has the potential to push sustainable energy technologies beyond their current limits. This program will specifically focus on designing efficient and affordable materials for sustainable catalysis and/or fuel cell applications by manipulating their fundamental properties. This critical research problem will be tackled with the aid of advanced modeling techniques, machine learning, and experimental collaborations. The person appointed will work as part of an interdisciplinary team focused on developing novel energy materials and will have access to world-class computational facilities and collaborations.
- Materials and Chemical Engineering
- Computational modeling
- Sustainable energy
- Catalysis Fuel cells
- Machine learning
- Research advisor: Dr. Kulbir Kaur Ghuman, Assistant Professor, Canada Research Chair (Tier 2) in Computational Materials Design for Energy and Environmental Applications
- Eligibility: A degree (M.Sc.) in Materials Science, Physics, Chemistry, or equivalent. Must be fluent in English or French (orally as well as in written). Be able to work independently as well as with a team. Should have ability to demonstrate critical and independent thinking will be invaluable assets. Should have relevant experience to the project (ML, DFT scientific writing etc)
Candidates with experience and strong background in machine learnings and material design will be contacted. This is a fully funded program.