Job Description

The Infrastructure Simulation SEnsing, Evaluation Laboratory (I-S2EE) investigates the opportunities provided by visual sensing technologies, computational mechanics, and artificial intelligence to tackle with the increasing multiplex problem in the existing built infrastructure. I-S2EE laboratory within the Department of Civil and Environmental Engineering at the University of Virginia seeks a Postdoctoral Research Associate to participate in a multi-disciplinary collaborative research projects related to informative digital twinning for large-scale structural systems. The candidate is expected to engage in research activities that leverage tools from the domains of vision-based experimental mechanics, model-based computational mechanics, and computational vision strategies such as deep learning, and mixed reality visualization. Prior experience with experimental testing of civil and/or mechanical systems is preferred and candidates with strong programming and analytical skills are encouraged to highlight relevance in the application materials.

The Postdoctoral Research Associate will work in a multidisciplinary environment and will collaborate with project team members from other disciplines including computer science and education. The postdoc is expected to engage in learning and research activities with high regard for your surroundings and ensure the preservation of a congenial work environment. Research progress will be monitored with regular meetings, progress updates, and dissemination of research findings through peer-reviewed journals. Excellent communication skills in both written and spoken English are desired.

Key Responsibilities

Conduct research under the supervision of Devin Harris, Ph.D., Professor and Chair of the Department of Civil and Environmental Engineering - Principal Investigator (PI). This specifically involves:

Technical

• Advocate cutting-edge visual sensing technology and data analysis to solve problems related to the built environment, with a particular focus on improving the resilience, sustainability, and safety of engineering structures and infrastructure.
• Collaborate with the team on multidisciplinary research consisting of image-based evaluation methods, visual recognition, data analytics, finite element simulations and experimental tests.
• Creating a process for identifying and predicting damage, considering both physical factors and surrogate models that utilize deep learning.
• Gaining insight into the fundamental mechanisms of structural damage propagation and fatigue.
  
  
  

Development

• The chosen candidate is required to take the lead and contribute to the development and composition of research proposals that receive external funding. This task involves identifying suitable funding opportunities, seeking collaborative partners, gathering initial data, and drafting proposals
• Assist supervisor in writing scholarly articles and research proposals.
• Conduct weekly meeting between PI, students and other researchers. This includes supporting supervision of graduate and undergraduate researchers to help guide their projects, progress, papers and to develop a path forward in their research.
  
  
  

QUALIFICATION REQUIREMENTS: Candidates must have a Ph.D. degree in a mechanics-based discipline such as civil engineering or mechanical engineering by the appointment start date. Individuals with specialized knowledge in artificial intelligence, machine learning, computer vision, data-driven structural identification, and image-based evaluation methods, including but not limited to digital image correlation, visual recognition, deep learning-based intelligent visual sensing, and experimental testing, will be given priority consideration. Additional preferred skills include sensor-based structural health monitoring, computational modeling and damage detection, mixed reality using AR/VR for finite element analysis and hands-on experience in a structural laboratory environment. The successful candidate will work directly with the PI and also engage in supervising current graduate and undergraduate students in the I-S2EE Lab.

APPLICATION PROCEDURE: Apply online at https://uva.wd1.myworkdayjobs.com/UVAJobs and attach a cover letter, curriculum vitae, a brief (1-page) statement of research interests, and the contact information for three individuals who can provide professional reference letters.

APPLICATION DEADLINE: Review of applications will begin on September 29, 2023, but the position will remain open until filled. The University will perform background checks on all new hires prior to employment.

This is a one-year appointment; however, the appointment may be renewed for an additional one-year increment, contingent upon available funding and satisfactory performance.

For questions regarding this position, contact Prof. Devin Harris, Professor and Chair – Civil and Environmental Engineering, at dharris@virginia.edu.

For questions regarding the application process, contact Rich Haverstrom, Faculty Search Advisor, at rkh6j@virginia.edu.

For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits.

With one of the highest graduation rates of minority undergraduate students and one of the highest percentages of women engineering students among public universities, the University of Virginia is fundamentally committed to increasing the diversity of its faculty and staff.

The University of Virginia, including the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician’s Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff. We believe diversity is excellence expressing itself through every person's perspectives and lived experiences. We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex (including pregnancy), sexual orientation, veteran status, and family medical or genetic information.