Field & Lab Research Assistant: Data, Sensors & Modeling

Texas A&M University System; Bryan (TX); On-site; USD 40,000 - 55,000; Full time

Benefits offered by this job

Relocation assistance

Tuition reimbursement

Flexible work hours

Job summary

Texas A&M Transportation Institute (TTI) seeks a Research Assistant to support diverse pavement analytics projects at the Bryan, TX campus. You will join the Pavement Analytics team, assisting in planning, scheduling, and executing laboratory and field research, including data collection, equipment setup, and non-destructive testing.

The role emphasizes rigorous data handling, modeling, and collaboration with students and senior researchers.

Qualifications

• Bachelor’s degree in engineering or related field.
• No work experience required.
• Familiarity with piezoelectric and Weigh-In-Motion sensors is a plus.
• Knowledge of software development including ML/AI is desirable.
• Willingness to travel for fieldwork as needed.

Responsibilities

• Fieldwork travels and data collection
• Equipment installation and sensor setup in the field
• Assist in design, planning, scheduling, and coordinating lab/field experiments
• Code programs and develop software for data processing and QC
• Data analysis, reporting, and documentation
• Maintain and manage research equipment and student labs
• Conduct computational modelling and simulations
• Perform laboratory testing and measurements
• Assist junior staff and other researchers
• Perform other duties as assigned by senior researchers

Skills

Field travel

Teamwork

Python

MATLAB

Machine Learning

Education

Bachelor’s degree in engineering

Tools

Abaqus

VBA

Job description

Texas A&M Transportation Institute (TTI) seeks a Research Assistant to support diverse pavement analytics projects at the Bryan, TX campus. You will join the Pavement Analytics team, assisting in planning, scheduling, and executing laboratory and field research, including data collection, equipment setup, and non-destructive testing.

The role emphasizes rigorous data handling, modeling, and collaboration with students and senior researchers.