Research Associate in Civil and Environmental Engineering

Research Associate in Civil and Environmental Engineering

The Infrastructure Simulation SEnsing, Evaluation Laboratory (I-S 2 EE) investigates the opportunities provided by visual sensing technologies, computational mechanics, and artificial intelligence to address the compounding challenges facing existing built infrastructure. The I-S 2 EE laboratory within the Department of Civil and Environmental Engineering at the University of Virginia seeks a Postdoctoral Research Associate to participate in multidisciplinary 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, computational vision strategies such as deep learning, and mixed reality visualization. We expect the successful candidate to bring genuine depth in one of these areas, either computational and AI methods or experimental mechanics, rather than mastery of all of them, and to be motivated to build fluency in the others alongside the project team.

Applicants are encouraged to use their materials to make the case for the strengths they do bring in these areas or complementary areas. The Postdoctoral Research Associate will work in a multidisciplinary environment and will collaborate with project team members from other disciplines. The postdoc is expected to engage in learning and research activities with high regard for their 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.

Key Responsibilities:

• Advance 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.
• Create a process for identifying and predicting damage, considering both physical factors and surrogate models that utilize deep learning.
• Develop insight into the fundamental mechanisms of structural damage propagation and fatigue.
• Contribute to the engineering education research thread of the project, in collaboration with education team members; examples include instrument development, assessment of student learning, or dissemination of education findings.

Development:

• The candidate will be encouraged to take a leading role in developing and writing research proposals that seek 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 meetings among the 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 hold a Ph.D. by the appointment start date in civil engineering, mechanical engineering, computer science, or a closely related field, including data science, systems engineering, or engineering education. This project spans experimental, computational, and educational work, and we do not expect any single candidate to be expert in all three. Competitive applicants will demonstrate clear depth in at least one of the following areas and a genuine interest in collaborating across the others:

• Computational and AI methods: artificial intelligence and machine learning, deep learning-based intelligent visual sensing, computer vision, mixed reality (AR/VR), data-driven structural identification, and image-based evaluation methods.
• Experimental mechanics: structural and materials testing in a laboratory environment, instrumentation of civil and/or mechanical systems, sensor-based structural health monitoring, digital image correlation, computational modeling and damage detection, visual recognition, and robotic inspection.
• Engineering education research (complementary): training or research experience in engineering education, the learning sciences, or educational assessment, held alongside a foundational engineering or computing discipline. We view this as a complementary strength rather than a stand-alone qualification, and candidates who bring it should describe how it connects to their technical training.

Strength in more than one of these areas is welcome but is not required. Applications will be evaluated on the depth of the candidate's primary area together with evidence of successful interdisciplinary collaboration. Strong written and spoken English…