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

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

Level:  Open to Postdoctoral and Senior applicants