Dr. Renzun Zhao in the Department of Civil, Architectural, and Environmental Engineering at North Carolina A&T State University is seeking a fully-funded PhD student to join his Solid Liquid Innovation Lab (SLI-Lab), starting Fall 2027. The position is supported by a new NSF grant that combines lab experimentation with machine learning to predict how PFAS ("forever chemicals") behave in engineered and natural environments, a challenge that current models are too limited to solve given the thousands of PFAS compounds in existence.
The student will run lab experiments to measure how different PFAS compounds interact with treatment materials, using a state-of-the-art Quadrupole Time-of-Flight mass spectrometer (QTOF-MS)(https://sites.google.com/view/ncatqtofcenter/home?authuser=0), combustion ion chromatography (C-IC) and other advanced instrumentation (https://renzunzhao.weebly.com/research-facilities.html), and will build machine learning models that turn experimental data into practical predictions for groundwater plume, water/wastewater treatment and site cleanup. The student will also work closely with faculty in data science to receive hands-on training in programming, machine learning, and AI more broadly, gaining a strong quantitative skill set alongside their environmental engineering expertise. There is also an option to branch into a related project studying PFAS in biosolids, and students are encouraged to bring their own research ideas aligned with the group's goals. Preference will be given to candidates with backgrounds in environmental engineering, chemical engineering, chemistry, data science, machine learning, or related fields, and with interest or experience in analytical instrumentation (e.g., MS, IC) and/or programming (e.g., Python).
Interested applicants should review the Computational Science and Engineering PhD program at North Carolina A&T State University (https://www.ncat.edu/academics/graduate-programs/coe/computational-scie…) and contact Dr. Zhao (rzhao@ncat.edu) with a CV, brief statement of research interests, and unofficial transcripts. Learn more about the SLI-Lab at https://renzunzhao.weebly.com/.