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Graduate Research Assistantship

Graduate Assistantship Position in Computer Science at University of Idaho, Idaho Falls

The construction sector is a major contributor to the U.S. economy. It represents over 4% of the U.S. GDP, equivalent to 1.36 trillion dollars. Despite recent advancements, studies indicate that the construction industry is still one of the least computerized sectors compared to manufacturing, telecommunication, and retail industries. In line with this, the construction research community faces significant challenges when it comes to adopting new data science and Artificial Intelligence (AI) solutions and techniques. Several factors, such as complexity and uniqueness of construction projects, the rapid pace of evolution of such technologies, and unfamiliarity of traditional construction faculty with such topics, cause this situation. As a result, it is crucial for researchers and academic experts to develop novel educational and training strategies to bridge such research skills gaps and strengthen construction research data literacy and digital fluency. This project addresses the above challenges faced by educators and researchers in construction engineering through the development of an educational and community engagement platform called CyCon (Cyber Construction).

The CyCon platform focuses on state-of-the-art AI/ML techniques and cloud computing solutions that serve CI (CyberInfrastructure) users, contributors, and professionals in the construction research community. The CyCon platform contains the following four training and educational components: (1) CI user resource modules, including modular learning coursework and course project libraries, to train CyCon users with basic and advanced CI research skills, (2) CI educator resource modules that provide novel strategies and online materials to efficiently train educators, (3) CI competition modules to help CyCon users practice and further advance their knowledge about AI/ML products and tools using real-world construction problems, and (4) CI crowdsourcing modules that provide a well-defined data pipeline using data warehouses to enrich construction datasets available for public use. The CyCon platform is designed as a sustainable educational and community engagement framework for the construction CI research community and is expected to significantly improve the learning and teaching experiences of CI users and educators at various construction programs nationwide.

The graduate student will be responsible and contribute to:
1) Web-based platform development, and
2) AI/ML, cloud computing, and visualization techniques development for the platform.

• Background and interest in machine learning and artificial intelligence disciplines,
• Software packages: Python, HTML, and Java,
• Ability to work independently and in a team, and strong communication skills,
• Publish peer-reviewed conference proceedings and archival journal articles, and
• Contribute to grant proposal developments.

• The appointment is for 4 years, contingent upon satisfactory performance after 12 months in the degree program and research performance.
• The hourly pay rate is approximately $19.6 per hour for 20 hour per week.
• This position covers tuition/fees and is eligible for health benefits.

  • Successful completion of satisfactory criminal background and reference checks.
  • Upon completing the I-9 Form on or before your first day of work, which verifies you are eligible to work in the United States.
  • Successful completion of pre-employment drug testing with satisfactory results.
  • Successful completion of all other pre-employment processes.
  • This appointment is contingent on continued funding and/or work to support the position.
  • This appointment is contingent on satisfactory progress in the degree program.
  • Employee acknowledges and agrees that in the event of insufficient funding and/or work, as determined by University in its sole discretion, University may terminate this Agreement and employee’s employment prior to the end of the term of appointment, upon 60 days written notice. Employee further acknowledges and agrees that in the event termination for insufficient funding and/or work becomes necessary, the notice provisions of Faculty Staff Handbook 3900B will not apply.

Interested individuals who have any questions are encouraged to contact Dr. Mirkouei at ( or (208) 757-5420. You can find more information about Mirkouei’s research group at