**This position is eligible for remote work, but on-site work is also an option.**
INTRO
The incumbent will be a Junior/Intermediate professional with experience in advanced atomistic scale modelling of structural materials. The candidate will be supporting the Federal Science and Technology program under the theme of advanced reactor systems and small modular reactors. They will propose new work that aligns with federal and industry demands. Additionally, they will support/propose completion of commercial contracts from industry groups.
The successful candidate will be required to contribute computational modelling expertise on structural materials relevant to contemporary and advanced nuclear reactors under various operating conditions. The candidate will also develop simulations and deliver recommendations on the behaviour of various reactor components for the safety and efficiency of the current Canadian reactor fleet and advanced reactor technologies. The candidate may be tasked with using computational results to guide experiments to provide data for model inputs and to verify predictions. The candidate will be expected to engage in continuous learning to maintain current expertise, knowledge, and history of nuclear reactors in Canada to support the industry and the regulator to resolve technical issues.
DUTIES
- Develops and applies atomistic scale simulations to investigate the behaviour of structural materials in conditions relevant to nuclear reactor systems, including high temperatures, impurities, oxidation, corrosion, etc.
- Exploits appropriate tools such as molecular dynamics, density functional theory-based modelling, and kinetic/object Monte Carlo codes.
- Expertise in the usage of the reactive force-fields (e.g., ReaxFF, artificial neural network-based potentials) is considered an advantage.
- Experience with machine learning approaches applied to materials modelling and simulation is an asset.
- Checks model predictions against expected behavior and experimental data for correctness.
- Assists in the assurance of safe and efficient use of structural materials through mathematical and computational models.
- Proposes new or refined models and experiments to answer key questions pertinent to customers or stakeholders.
- Communicates and disseminates generated information both internally to other CNL research groups, and externally in peer-reviewed publications and technical presentations.
- Contributes modelling expertise to internal and external customers.
QUALIFICATIONS
- PhD degree in physics, materials science or applied science from a university of recognized standing
- A minimum of 2 years of experience in materials behavior subjected to elevated temperatures, impurities and/or oxidation/corrosion environments is required.
- Demonstrated expertise in atomistic scale modelling techniques, such as VASP, Quantum ESPRESSO, or LAMMPS is essential.
- Experience developing new modelling techniques (e.g. ReaxFF, Monte Carlo, QM/MM) and their application of them to materials science.
- Expertise in modelling techniques such as hybrid Grand Canonical MC/Molecular dynamics, uniform-acceptance force-biased MC, parallel replica dynamics, and adaptive accelerated ReaxFF reaction dynamics is desirable.
- A strong command of the English language both written and oral is required for this position.
The successful candidate will be an enthusiastic self-starter who enjoys working with a team of computational material science professionals and technologists. Working knowledge of (python and/or C/C++) programming, word processing and presentation software is required.
CNL is committed to providing an atmosphere free from barriers that promote equity, diversity and inclusion in achieving our mission. CNL welcomes and celebrates employees, stakeholders and partners of all racial, cultural, and ethnic identities.
CNL also supports a workplace environment and a corporate culture built on our Core Values: Respect, Teamwork, Accountability, Safety, Integrity and Excellence, which encourage equitable employment practices and career prospects inclusive of accommodations for all employees.
CNL is committed to being an equal-opportunity employer. If you require accommodation measures during any phase of the hiring process, please inform the Talent Acquisition Specialist with whom you are in contact. All information received in relation to accommodation requests will be kept confidential.
The Chalk River Laboratories site is located on the unceded and unsurrendered territory of the Algonquin Anishinaabe people. As an organization, CNL recognizes and appreciates their historic connection to this place. CNL also recognizes the contributions that First Nations, Métis, Inuit and other Indigenous Peoples have made, both in shaping and strengthening this community in particular, and this province and country as a whole.
