Research Associate bei University System of New Hampshire

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The Research Associate will support faculty-led research in the Department of Civil and Environmental Engineering with emphasis on structural engineering, digital twinning, advanced computational modeling, and experimental validation. The position contributes to ongoing federally and state-funded projects by assisting in experimental testing, data analysis, literature review, software development, and preparation of technical reports and publications. This is a part-time, hourly position intended to leverage the candidate’s advanced training in civil and structural engineering research. This is a nonessential staff position.

Duties/Responsibilities

Research Support (40%)

• Conduct laboratory and field experiments on structural systems, including instrumentation, data collection, and test execution.

• Perform structural modeling, simulation, and validation activities.

• Assist with analysis of large-scale experimental and monitoring data.

• Support digital twin and machine learning–based methods for civil infrastructure.

Scholarly Contribution (25%)

• Conduct literature reviews and summarize recent advances in structural engineering, digital twinning, and artificial intelligence applications.

• Draft, edit, and prepare manuscripts for journal and conference submissions.

• Assist in the preparation of research proposals and technical reports.

Programming & Data Analytics (20%)

• Develop and implement algorithms for system identification, condition assessment, and structural health monitoring.

• Apply statistical and computational tools to assess research hypotheses.

Mentoring & Collaboration (15%)

• Provide guidance to undergraduate and graduate students involved in related projects.

• Collaborate with faculty, postdoctoral researchers, and project partners on joint tasks.

Requirements

Minimum Acceptable Education & Experience:

• Ph.D. in Civil Engineering, Structural Engineering, or a closely related field.

• Demonstrated research experience in structural mechanics, structural health monitoring, experimental testing, or computational modeling.

Knowledge, Skills & Abilities:

• Strong background in structural analysis, system identification, and digital twinning.

• Proficiency in MATLAB, Python, or similar computational tools.

• Ability to design and conduct laboratory and/or field experiments, including instrumentation and data collection.

• Ability to analyze and interpret experimental and monitoring data.

• Excellent written and oral communication skills.

• Ability to work independently and collaboratively within a research group.

Preferred Qualifications:

• Prior peer-reviewed publications in structural health monitoring, system identification, or digital twin applications.

• Experience with proposal writing and interdisciplinary collaboration.

• Familiarity with machine learning methods and high-performance computing applications in engineering.

The University of New Hampshire is an R1 Carnegie classification research institution providing comprehensive, high-quality undergraduate and graduate programs of distinction. UNH is located in Durham on a 188-acre campus, 60 miles north of Boston and 8 miles from the Atlantic coast and is convenient to New Hampshire’s lakes and mountains. There is a student enrollment of 13,000 students, with a full-time faculty of over 600, offering 90 undergraduate and more than 70 graduate programs. The University actively promotes a dynamic learning environment in which qualified individuals of differing perspectives, life experiences, and cultural backgrounds pursue academic goals with mutual respect and shared inquiry.

EEO Statement

The University System of New Hampshire is an Equal Opportunity/Equal Access employer. The University System is committed to creating an environment that values and supports diversity and inclusiveness across our campus communities and encourages applications from qualified individuals who will help us achieve this mission. The University System prohibits discrimination on the basis of race, color, religion, sex, age, national origin, sexual orientation, gender identity or expression, disability, genetic information, veteran status, or marital status.

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