About the Position
The Department of Civil Engineering at the University of Memphis invites applications for one fully funded Ph.D. position in Environmental Engineering, starting either Spring 2025 or Fall 2025. Research areas encompass a variety of topics, including water quality sensor design (nutrients, metals, PFAS, etc.), wastewater treatment techniques (biological process, membrane filtration, etc.), and machine learning applications in environmental engineering. The Ph.D. students will be supervised by Dr. Yuankai Huang (https://www.memphis.edu/ce/people/huang.php).
The position covers tuition, offers competitive stipends, and includes benefits such as health insurance and conference travel fees. Students will work with interdisciplinary research teams at the University of Memphis to address critical environmental challenges. Dr. Huang will provide tailored mentoring plans based on the student's research background and career goals and fund professional development activities.
How to Apply
We welcome candidates with a bachelor's degree in Environmental Engineering, Civil Engineering, Chemical Engineering, or a related field. Ideal candidates should be self-motivated, able to work independently or as part of a team, and possess strong communication skills. While a Master's degree is preferred, it is not required.
Interested candidates are encouraged to email Dr. Yuankai Huang (yuankai.huang@memphis.edu) with "UofM CE Graduate Application" as the subject. Please attach a single PDF containing your CV (with publications if possible), transcripts, TOEFL/IELTS scores (for international candidates), and contact information for three references. Applications will be reviewed on a rolling basis. Admission requirements are available at: https://www.memphis.edu/ce/future-students/phd.php
About Dr. Yuankai Huang
Dr. Yuankai Huang is currently an Assistant Professor in the Department of Civil Engineering at the University of Memphis. His research tackles crucial water-resource-energy-sensor-data challenges. He focuses on real-time water quality monitoring, developing sustainable water treatment technologies, and employing computational methods in the Water-Energy-Food Nexus.