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innovative materials for transportation infrastructure with applications in civil (geotechnical/material) engineering, industrial scale economics, and transportation infrastructure performance. The successful
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Qualifications Minimum Education and Experience This position requires PhD in transportation engineering, industrial engineering, energy engineering, or related engineering fields. The candidate should have skills
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visit our lab website: https://sites.rutgers.edu/tina-liu-lab/ Under the direction of the Principal Investigator (PI), Dr. Tina Liu, the Postdoctoral Associate will: • Develop and pursue research projects
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innovative materials for transportation infrastructure with applications in civil (geotechnical/material) engineering, industrial scale economics, and transportation infrastructure performance. The successful
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development work on a carbon dot technology. The work requires knowledge of materials synthesis, polymer and carbon material properties, materials analysis, and characterization. Additionally, assistance in
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, make discoveries, serve as an engine of economic growth, and generate ideas for improving people's lives. Connections working at Rutgers University More Jobs from This Employer https://main.hercjobs.org
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an anticipated start date of May 2026 in Dr. Nicole Fahrenfeld’s group in Civil & Environmental Engineering at Rutgers University-New Brunswick (https://sites.google.com/site/nicolefahrenfeld/home ). The postdoc
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and primary care. Provides input on study design and develops analytic plans. Works with staff, faculty, and consultants to assess, evaluate, and disseminate the results from the study, participates in
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date of May 2026 in Dr. Nicole Fahrenfeld’s group in Civil & Environmental Engineering at Rutgers University-New Brunswick (https://sites.google.com/site/nicolefahrenfeld/home ). The postdoc will lead
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, Public Administration, Psychology, Industrial/Systems Engineering, or related fields critical to IDRIS Experience in analyzing real data Strong programming skills Familiarity with statistical methods