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RAP opportunity at Federal Highway Administration     FHWA

Use of Data Science in Asphalt Materials

Location

Federal Highway Administration

opportunity location
27.01.00.C0690 McLean, VA 221012296

Advisers

name email phone
Maryam Sadat Sakhaei Far Maryam.Sakhaeifar@dot.gov 919 673 0323

Description

The Federal Highway Administration's Turner-Fairbank Highway Research Center (TFHRC) is seeking a postdoctoral associate with an expertise in applying machine learning to asphalt materials.  The ideal candidate possesses specialized experience with respect to:

  • Laboratory evaluation of asphalt materials (mixture and binder preferred);
  • Relating field performance data to the laboratory-measured data for asphalt materials;
  • Data mining and database management;
  • Developing and interpreting artificial neural network approaches to model behavior of asphalt materials; and
  • Understanding of performance specifications and the role of volumetrics and component materials in long-term pavement performance.

The use of machine learning offers potential to the asphalt pavement community.  Additionally, TFHRC offers a unique dataset to advance this important topic, including but not limited to performance information from the Long-Term Pavement Performance database and the Asphalt Binder and Mixture Laboratory (ABML) at TFHRC. Possible applications include characterization of emerging technologies and additives, optimizing construction processes, and long-term pavement performance prediction.  The associate will be expected to advance FHWA's goals in data science applications to asphalt materials.

key words
Machine Learning, Artificial Neural Network, Laboratory Evaluation, Pavement Performance Models, Asphalt Concrete

Eligibility

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens
Level:  Open to Postdoctoral and Senior applicants

Stipend

Base Stipend Travel Allotment Supplementation
$67,000.00 $4,000.00

Experience Supplement:
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.

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