Research Associate - Interdisciplinary Research in Civil and Environmental Engineering

The Center for Emerging Technology Identification for Roadway Infrastructure Construction & Maintenance ("The Center"), is focused on mission-oriented technologies of the present and future and their evolution toward technology native roadway infrastructure systems of tomorrow. The Center represents a collaboration between the University of Virginia (UVA) and the Virginia Transportation Research Council (VTRC).

The Center for Emerging Technology Identification for Roadway Infrastructure Construction & Maintenance is focused on mission-oriented technologies of the present and future. The Center aims to drive exploratory research at the intersection of technology and roadway infrastructure, focusing on understanding the transition process for technology-native roadway infrastructure and identifying the correct timing for implementation. The Center leverages the combined strengths of VTRC and UVA to identify and assess synergistic opportunities with the potential for future implementation in the Commonwealth's roadway infrastructure. The team is seeking two post-doctoral research associates to support the activities of the Center.

Key Responsibilities

The post-doctoral researchers will play a crucial role within the Center for Emerging Technology Identification for Roadway Infrastructure Construction & Maintenance. Their responsibilities will include:

• Technology Evaluation: Engaging in comprehensive industry scans to evaluate the readiness and maturity of emerging technologies pertinent to roadway infrastructure. This includes sensing, measurement, and evaluation; artificial intelligence/machine learning; simulation and modeling; robotics/autonomous systems; and advanced testing approaches.
• Exploratory Research: Conducting cutting-edge preliminary research at the intersection of technology and roadway infrastructure. This involves investigating the transition process for technology-native roadway infrastructure and identifying the optimal timing for implementation.
• Documentation and Dissemination: Synthesizing research findings into concise white papers, technical memos, video shorts, and webinars. The goal is to create a technology knowledge base that guides strategies for research initiation and implementation.
• Communication and Awareness: Raising awareness and educating engineers on new transportation technologies through compact presentations, invited technical speakers, and other information-sharing methods.
• Collaboration: Serving as the collaborative bridge within the Center team, the post-doctoral researchers will work closely together and with the faculty team members. They will actively communicate findings across disciplinary boundaries, ensuring that insights and discoveries are shared effectively among team members from diverse fields such as civil engineering, mechanical engineering, electrical engineering, computer science, and data science. This interdisciplinary collaboration is essential for driving innovation and achieving the Center's mission.

These responsibilities are designed to ensure that the post-docs contribute significantly to the Center's mission of driving innovation and preparing for the future of roadway infrastructure.

QUALIFICATION REQUIREMENTS: Candidates must have a Ph.D. degree in related fields of engineering (civil, mechanical, electrical), computer science, or data science by the appointment start date. Individuals with specialized knowledge in one or more of the following areas will be given priority consideration:

Computational Technology and Informatics

• Artificial intelligence
• Machine learning
• Computer vision
• Computational geometry
• Low-power sensing systems
• Embedded hardware
• Low-level software
• Low-power wireless communication

Structural Mechanics and Smart Materials

• Deployable Robotics and Autonomous Systems
• Remote sensing and monitoring
• Embedded sensing and materials collaboration
• Smart materials
• Deconstruction/reuse
• Condition Assessment
• Modeling/surrogate modeling