Research Associate, Center for Transportation Research, Cockrell School of Engineering

Job Posting Title:

Research Associate, Center for Transportation Research, Cockrell School of Engineering

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Hiring Department:

Fariborz Maseeh Department of Civil, Architectural and Environmental Engineering

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

Exempt

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Earliest Start Date:

Feb 01, 2025

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Position Duration:

Expected to Continue

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Location:

PICKLE RESEARCH CAMPUS

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Job Details:

General Notes

As a top-10 engineering school with the No. 1 program in Texas, the Cockrell School of Engineering at The University of Texas at Austin has been a global leader in technology innovation and engineering education for over a century. The Center for Transportation Research (CTR) is a multidisciplinary and multimodal research institute situated within the Cockrell School.

With 11 undergraduate and 13 graduate programs, over 20 research centers and a faculty community that boasts one of the highest number of National Academy of Engineering members among U.S. universities, Texas Engineering has launched some of the nation's most accomplished leaders and pioneered world-changing solutions in virtually every industry, from space exploration to energy to health care. Situated in the heart of Austin - named "America's Coolest City" by Expedia and "The Best Place to Live in the U.S." by U.S. News and World Report - the Cockrell School embodies the city's innovative spirit. Major companies with Austin campuses, such as Dell, National Instruments, Apple, IBM, Samsung, Google, and many others, continue to recruit Cockrell School students at a remarkable rate, launching thousands of successful careers and developing Texas Engineers into industry leaders.

At UT Austin, we say “What starts here changes the world.”  As a member of the university community, you will be part of an organization that is internationally recognized for our academic programs and research. Your work will have meaning and make a difference not only in the lives of our faculty, staff, and students, but also those who are impacted by our first class academic and research programs.

The position is expected to end on August, 31/2026.

UT Austin, recognized by Forbes as one of America’s Best Large Employers, provides outstanding employee benefits and total rewards packages that include:

• Competitive health benefits (employee premiums covered at 100%, family premiums at 50%)

• Voluntary Vision, Dental, Life, and Disability insurance options

• Generous paid vacation, sick time, and holidays

• Teachers Retirement System of Texas, a defined benefit retirement plan, with 7.75% employer matching funds

• Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b)

• Flexible spending account options for medical and childcare expenses

• Robust free training access through LinkedIn Learning plus professional conference opportunities

• Tuition assistance

• Expansive employee discount program including athletic tickets

• Free access to UT Austin's libraries and museums with staff ID card

• Free rides on all UT Shuttle and Austin CapMetro buses with staff ID card

Purpose

To conduct transportation-related research within the smart structures research laboratory on ultrasonic sensing methods for damage detection in infrastructure systems, in particular metallic structures. This should include both hardware and software development.

Responsibilities

• Assist in the development of research proposals and technical reports and technology-based projects.

• Conduct project meetings, writing publications.

• Support the supervision of undergraduate and graduate research assistants in conducting research activities.

• Present research at professional conferences.

• Other duties as required.

Required Qualifications

• Ph.D. in Civil/Mechanical Engineering or closely related fields.

• Prior working experience in ultrasonic sensing methods for damage assessment, modeling wave propagation in metallic structures, and machine learning methods.