Improving combustion engines and industrial chemical reactors requires a detailed understanding of complex chemical reaction networks. The project aims to radically increase the pace of innovation by developing machine learning tools to engineer chemical reactions.  The student will work with “big data” sets comprising various reaction properties.  These large data sets will include experimental values augmented with simulated values generated with multi-scale models. Artificial neural networks and genetic algorithms will then be trained to predict a wide range of combustion properties using a limited number of input parameters.  The machine learning predictions will be compared against detailed multi-scale models to develop novel cloud-based tools for understanding combustion in complex systems.  The candidate will develop new software incorporating new uncertainty-analysis features, experimental templates, open-access formats, and open- source software for data mining and predictive simulations.  The candidate will receive scientific guidance in developing the various aspects of the cyber-infrastructure, but must have the appropriate skills, technical expertise, and prior experience to be successful. Excellent chemical engineering and computer programming skills are required.

https://cloudflame.kaust.edu.sa

 ​​​

The student is expected to participate in writing journal publications and presenting research at conferences.

​Weekly updates on research progress

Presentation of your research at least three times during course of internship

Remaining in the lab/office during regular business hours (9am to 5pm)

Written final report on internship projects.​