Research Associate

The final salary offer may be determined by several factors, including, but not limited to, the candidate's qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university's broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.

Rutgers offers a comprehensive benefits package to eligible employees, based on position, which includes:

For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview.

The Rutgers Infrastructure Monitoring and Evaluation ( RIME) Group has been developing expertise over the past decades in the application of 1) Structural Health Monitoring ( SHM) and Weigh-In-Motion ( WIM) systems integrated with advanced probabilistic methods, machine learning and Artificial Intelligence (AI) approaches, and 2) advanced Finite Element (FE) analysis with particular emphasis on the use of LSDYNA for simulation of crash analysis and roadside safety. This work increasingly emphasizes reliability-based performance assessment, load effects characterization, and data-informed evaluation frameworks consistent with AASHTO LRFD BDS and AASHTO MBE provisions for bridge design, evaluation, and load rating.

The RIME Group is seeking a Research Associate for the above areas of research and to work on faculty-led, externally funded research projects. The primary responsibilities include synthesizing prior and ongoing research results and extending them toward practical testing, analysis, reliability assessment, and load rating procedures that bridge owners and managers can adopt as part of comprehensive asset management and preservation strategies. The successful candidate will contribute to the development, validation, and fusion of SHM/ WIM-informed analysis methodologies for bridge evaluation, including probabilistic modeling, reliability analysis, and load rating under AASHTO LRFD BDS and MBE frameworks. Both positions involve close collaboration with federal, state, and local agencies, universities, and private-sector partners.

Key Duties and Responsibilities
30%1. Develop and integrate SHM/ WIM-informed bridge evaluation and load rating methodologies consistent with AASHTO LRFD BDS and AASHTO MBE.
20%2. Analyze WIM data and characterize truck loads, load effects, and fatigue demand to support data-driven reliability and rating.

20% 3.Perform advanced FE analyses using commercial packages (e.g., ABAQUS, LS- DYNA) to evaluate structural response and support specialized simulations (e.g., crash/roadside safety when applicable).

20%4. Support practical testing and validation (laboratory and/or field) related to SHM sensors/long-term monitoring and structural/concrete member performance when applicable

10%5. Writing proposal, reports, and papers.

The ideal candidate will hold a Ph.D. in Structural Engineering with a strong background in AASHTO-based bridge design, evaluation, and load rating, including experience with LRFD and MBE provisions, reliability analysis, probabilistic modeling, and capacity and demand assessment. Extensive experience with WIM technologies, truck load characterization, fatigue analysis of steel bridges, and data-driven reliability and load rating methodologies is strongly preferred. Experience with FE analysis using commercial software packages such as ABAQUS and LS- DYNA is highly desirable. Knowledge of machine learning (ML) and large language models ( LLM) and experience with SHM systems, sensor technologies, model updating, and long-term performance monitoring are desirable. In addition, candidates with experience in advanced laboratory testing of concrete materials and structural members, as well as field implementation of advanced concrete technologies in bridges and infrastructure, are highly desirable. The selected candidate(s) will work closely with other researchers within the RIME Research Group and with partners at the USDOT Tier 1 University Transportation Center, C2SMARTER, led by New York.

Extensive WIM experience (truck load characterization; fatigue analysis; data-driven reliability/load rating).

Abaqus, LS- DYNA, Machine Learning.

Primarily office/computer-based research with periodic laboratory and/or field activities as needed to support testing, SHM/ WIM implementation, and partner coordination.

Interested candidates should submit the following materials: 1) an explanation of their research interests and how their background and experience match the described qualifications, 2) detailed curriculum vitae, 3) names and contact information including email addresses for at least three referees, 4) academic transcripts, and 5) an abstract of the doctoral dissertation.