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.

Keywords:

Artificial neural network; Machine learning; Pavement performance; Pavement prediction; Laboratory evaluation; Asphalt concrete; Asphalt mixture; Asphalt;

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens

Level:  Open to Postdoctoral and Senior applicants