Research Assistant - Sustainable Engineering Asset Management (SEAM) Research Group

The Research Institute of Sciences and Engineering (RISE) invites applications for a full-time Research Assistant to join the Sustainable Engineering Asset Management (SEAM) Research Group.

SEAM is an interdisciplinary research group conducting research in the area of quality, reliability, human factors, supply chain management, AI and machine learning, civil engineering assets, maintenance engineering, and management.

A Research Assistant helps SEAM researchers by conducting various supporting research activities or performing experiments under their supervision. He/she also helps in the administrative aspects related to the projects and the research group.

• Collaborate effectively with the research coordinator and team members to support the successful execution of research projects.

• Ensure the integrity, confidentiality, and secure management of sensitive research data in accordance with institutional and ethical standards.

  
  

• Conduct comprehensive literature reviews and synthesize relevant information from academic and public sources into concise analytical reports.

• Design, prepare, and execute experiments, including data collection, analysis, interpretation, and clear presentation of findings.

• Develop, maintain, and manage research databases, ensuring accurate documentation and accessible records of experimental data.

• Prepare research documentation and outputs, including grant submissions, progress reports, manuscripts, presentations, surveys, and research instruments.

• Provide academic and operational support, including assisting undergraduate and graduate students, attending project meetings and seminars, and managing project-related correspondence.

• Support administrative and financial management functions, including monitoring project budgets, contributing to annual reports, and assisting with research group operational tasks and staffing processes.
  
  

• Bachelor’s or Master’s degree in Engineering (preferably Industrial Engineering and Engineering Management), Computer Science, or Computer Engineering.

• Strong proficiency in computer applications, including Microsoft Office, SPSS, contemporary programming languages (e.g., Python), and commonly used tools for embedded engineering systems implementation.

• Experience in optimization and statistical analysis, with working knowledge of software such as MATLAB, Minitab, and/or machine learning techniques and tools.

• Prior experience in an academic or higher education environment within a similar setting is considered an asset.