INRS-EMT, Varennes, Québec (Canada)

Your mission:

The Insilco Matters Laboratory (http://www.insilicomatters.com) at the Institut National de la Recherche Scientifique (INRS) focuses on designing advanced materials and providing unprecedented understanding of materials and chemical processes from nano to electronic scales under various conditions. IML specializes in advanced computational tools in designing novel materials for various energy and environmental applications including fuel cells, catalysis, and CO₂ capture technologies.

This project is concerned with achieving the optimal properties of thin films of polycrystalline oxides such as doped-zirconia, and nitrides such as vanadium nitride, that have interesting microstructure due to zero to three dimensional defects and disorder present in them. In order to use oxides, specifically for application in solid oxide fuel cells, and nitrides for coatings, their micro/nano structure needs to be controlled in a multi-parameter space, which usually requires very high number of experiments and computational analysis. To reduce the cost and time required to design the desired thin films experiments, molecular dynamics (MD) simulations and machine learning (ML) will be combined in this project to predict the composition-structure-property relationship of thin films, thereby guiding experimentalists to synthesize the desired thin films. The candidate will focus on computational studies while experiments will be conducted in collaboration with various experimental teams. Beside achieving the objectives of this research, the candidate is expected to prepare verbal and written communications to present obtained results to academic and industrial collaborators as well as to the scientific community.

Required qualifications:

• PhD degree in physics, chemistry, materials science, chemical engineering, or in closely related fields,
• background in computational condensed matter physics, specifically classical molecular dynamics
• experience with using machine learning for predicting material properties.
• hands-on experience with common solid-state computational chemistry codes like LAMMPs, OVITO, etc. and machine learning environments like PyTorch.
• hands-on experience with data generation and management
• a strong grasp of solid-state physics, in particular oxide and nitrite physics
• experience with high-performance computing
• excellent oral and written communication skills in English
• self-motivated and independent
• willing to work collaboratively

Your desired qualifications:

• experience with high-throughput calculations
• experience with Density Functional Theory (DFT) calculations
• ability to communicate in French

What we offer:

INRS is a graduate-level university active in fundamental and applied research. It brings together 150 professors and nearly 800 graduate-level students and post-doctoral fellows in four centers located in Quebec City, Montréal, Varennes, and Laval. It is consistently ranked as one of the top universities in Canada in terms of research intensity (grants per professor) and offers a stimulating and supportive work environment provided by a tight-knit community of experts in their respective domains. The salary offered is $53,000 (CAD) per year. The employment contract is for one year and is renewable. The position is available immediately and will remain open until a suitable candidate is hired. The position is based at the Institut National de la Recherche Scientifique – Centre Énergie, Matériaux and Télécommunications (INRS-EMT) in Varennes, near Montreal. The work environment is bilingual (English and French).

How to apply:

Interested candidates should send a CV, including a detailed list of research publications, a cover letter clearly stating their interest and relevant research experience with the proposed topics, as well as the name and contact information of three references to:

Professor Kulbir Ghuman

Institut national de la recherche scientifique

Centre Énergie Matériaux Télécommunications

1650, boulevard Lionel-Boulet

Varennes (Québec) J3X 1P7 CANADA

[email protected]

Please mention if you are an international or a domestic (Canadian citizen/permanent resident) candidate. The LMN and IML laboratories value diversity and promote a research environment where individual differences are recognized, appreciated, respected, and valued. All qualified individuals are invited to apply, particularly members of visible and ethnic minorities, women, Indigenous people, and people with disabilities.