Scholarship: Full benefits as per university rule
Degree: BS, MS, PhD
Nationality: International Students
Application deadlines: January 15th 2023
Dr. Qiuhua Huang will join the Colorado School of Mines (CSM) as an Associate Professor in January 2023. Dr. Huang received his Ph.D. from Arizona State University in 2016, where he studied under Professor Vijay Vittal, a member of the American Academy of Engineering and a renowned expert in power system stabilization. He is currently working in R&D at Utilidata, an energy startup company. Prior to that, he worked full time at Pacific Northwest National Laboratory (PNNL) as project leader and co-leader of several DOE funded projects totaling about $10 million. He is currently on the editorial board of IEEE Transactions on Power Systems, the vice chair of IEEE PES Intelligent Data Mining and Analysis Working Group and the chair of the Simulation Tools Interface Technology Topic Group. Dr. Huang has many years of experience working in national laboratories and industry, and has established close relationships with several national laboratories, industry (including electric utilities and energy-related companies), and universities in the U.S., and can recommend internships and employment.
For more information, please visit his personal website: http://qiuhuahuang.weebly.com/
- power-centered energy system modeling and simulation, especially combining machine learning, digital twin, automatic differentiation and high performance computing
- main distribution network co-simulation, operation and control
- Distribution network operation, analysis and control for large-scale distributed energy access
- Edge computing and distributed artificial intelligence in power systems
- Interested in research, interested in the application of new technologies in the field of power and energy, with strong hands-on skills
- preferably have research experience related to the research direction of the subject group
- have some programming ability, familiar with Python, Java or C++ programming is better, preferably familiar with power system simulation or other related program development students.
- familiar with one or more common power system simulation software such as BPA, PSCAD, RTDS, PSS/E, PowerWorld, OpenDSS, GridLAB-D
- Machine learning background and some common machine learning tools such as Tensorflow, PyTorch, scikit-learn will be preferred
- If you are interested in applying, please send your resume, transcripts, personal statement and one representative essay (if any) to [email protected]. Please include “PhDApplication + Name + School of Graduation” in the subject line.