City: Mississauga
Organizational Unit: Clean Energy Innovation
Classification: RO
Tenure: Term
Duration: Until 31 March 2029
Language Requirements: English
Work arrangements:- Due to the nature of the work and operational requirements, this position will require full-time physical presence at the NRC work location identified.
At the NRC, we recognize that Indigenous candidates may have important connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC Hiring team using the contact information below.
The roleWe are looking for a Research Officer (RO) with experience in a variety of accelerated materials discovery and/or process optimization aspects to be at the forefront of our accelerated material discovery/process optimization platforms at the NRC Research Facility in Mississauga. The selected candidate would be someone who shares our core values of Integrity, Excellence, Respect and Creativity.
You will be working in a team environment with researchers and technical experts in world-class facilities. This work will involve you playing a leading role in the design, development, and orchestration of self-driving laboratories (material acceleration platforms) at Clean Energy Innovation Mississauga (CEI-M). This role will be highly collaborative, leading a project involving a multi-disciplinary team of mechatronics, design and automation engineers, electrochemists, process engineers, and AI/ML experts to realize self-driving laboratories towards new material discoveries.
In this role, you will be responsible for all aspects of the self-driving laboratory, including :
- the conception and direction of the experimental workflow,
- benchtop-to-automation/high throughput experimentation development as it relates to synthesis, characterization, and performance measurements
- development of the data handling and post-processing automation workflow
- working with AI/ML experts for optimization algorithm development and associated experts for integration into the experimental workflow.
You will work with a team that possesses focused skill sets (subject matter expert, AI/ML, robotics and automation etc.) to address specific areas of the project. However, you will serve as the primary orchestrator to realize a successful self-driving laboratory, and be directly involved in all aspects from conception, development, testing, operation, and deployment. You will lead autonomous materials discovery or process optimization campaigns, utilizing a validated self-driving laboratory, and also take on the role of project manager, and be responsible for project definition, budget, timeline, and regular reporting of project progress to program managers and team lead.
You will be involved in the development of new research proposals, management of research projects, maintenance of research partnerships with external academic, industrial and government collaborators and supervision of junior staff, students and vendors.
Being in a dynamic position means that the workdays are always evolving, challenging and motivating. You are a self-starter who can excel in an evolving and exciting R&D environment and provide input into the direction and priorities of R&D activities that will be set forth by this new initiative in Mississauga. If you are that person, this may be the perfect role for you!
Screening criteriaApplicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:
EducationPh.D. in Chemistry, Physics, Engineering, or similar domain.
For information on certificates and diplomas issued abroad, please see Degree equivalency
Experience- Recent* experience in the materials domain for clean energy technologies (carbon capture, energy storage, and/or other related applications) with recent experience regarding the synthesis, modification, characterization, and performance testing of these materials
- Experience** with concepts of laboratory automation and/or hands-on experience working with automated equipment in the context of clean energy technologies
- Experience** with AI/ML approaches and data science, in particular as it relates to their application with experimental data and/or experimental optimization
- Significant*** experience in the preparation of scientific publications and new research proposals
- Recent* experience in leading research projects involving a multidisciplinary team. Experience working in an agile environment would be considered an asset
- Experience** with robotics and mechatronics woud be an asset.
* Recent experience is defined as within the last three (3) years.
** Experience is defined as one (1) to three (3) years of experience.
*** Significant experience is defined as three (3) to five (5) years of experience.
Condition of employmentReliability Status
For a Reliability Status, verification of background information over a period of 5 years is required.
Language requirementsEnglish
Information on language requirements and self-assessment tests
Assessment criteriaCandidates will be assessed on the basis of the following criteria:
Technical competencies- Ability to conduct materials research, including the synthesis, modification, characterization, and performance evaluation, using methods that are relevant to clean energy technologies.
- Knowledge of machine learning / AI methods and their applications to experimental data;
- Knowledge of data science (statistics and quality control) as it relates to experimental data and ability to automate data storage, curation, and post-processing of experimental results
- Demonstrate knowledge of common clean energy technologies as evidenced by a strong publication record in high-impact journals
- Applies advanced programming capabilities (1-3 years minimum experience) in languages relevant to accelerated materials discovery, particularly Python
Behavioural competencies- Research - Communication (Level 3)
- Research - Creative thinking (Level 3)
- Research - Teamwork (Level 2)
- Research - Results orientation (Level 3)
- Research - Initiative (Level 2)
Competency Profile(s)For this position, the NRC will evaluate candidates using the following competency profile(s): Research;
View all competency profiles
CompensationThe intent of this hiring action is to staff this position at the
AsRO to AcRO level, which is
an mid-career level position with a salary range of
$83,985 to
$139,608.
NOTE: Please note that the full RO/RCO salary scale has five levels. Salary determination will be based on a review of the candidate's expertise, outcomes and impacts of their previous work experience relative to the requirements of the level. As such, the initial salary could be within another level of the
RO/RCO salary scale (i.e. above
or below the intended level for this position).
NRC employees enjoy a wide-range of competitive benefits including a robust pension plan, comprehensive health and dental coverage, disability and life insurance, office closure at the end of December, and additional supports to enhance your well-being throughout your career and beyond.
Notes- Relocation assistance will be determined in accordance with the NRC's directives.
- A pre-qualified list may be established for similar positions for a one year period.
- Preference will be given to Canadian citizens and permanent residents of Canada. Please include citizenship information in your application.
- The incumbent must adhere to safe workplace practices at all times.
- We thank all those who apply, however only those selected for further consideration will be contacted.
Please direct your questions, with the requisition number (25325) to:
Telephone: 3439909603
Closing Date: 12 November 2026 - 23:59 Eastern Time
For more information on career tools and other resources, check out Career tools and resources
*If you are currently a
term or continuing employee at NRC, please apply through the SuccessFactors Careersmodule from your NRC computer.
