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Ph.D. Student for Fall 2026

I am looking for 1-2 Ph.D. students who are committed to research on:

Generative AI and Large Language Models for energy applications
Emerging Large (AI data center, EV charging stations) load characterization
Smart grid data analytics and situational awareness

Ideal Candidate:

Bachelor’s or Master’s degree in EE, CS, or related fields
Background in machine learning, power systems, or related fields
Proficiency in Python, C++, MATLAB, or similar programming tools
Interest in top-tier publications and open-source research

If you are interested, please send your CV/resume and unofficial transcripts (undergraduate and graduate, if applicable) to yhu6@mtu.edu

Email Subject: Prospective Ph.D. Student - [Your Full Name] - [Starting Semester]

Master Students

If you are a current master student interested in getting involved in research, please reach out with your CV and transcripts!

Note that students without the desired background will still be considered provided they show interest/passion.

Outstanding applicants will be supported as GTA or GRA.

Undergraduate Students

If you are a current undergraduate interested in getting involved in research, please reach out with your CV and transcripts!

Note that students without the desired background will still be considered provided they show interest/passion.

There are some related programs at Michigan Tech:

Undergraduate Research Internship Program (URIP)
Summer Undergraduate Research Fellowship (SURF)
Research Experience for Undergraduates (REU)

Current Group Members

M.S. Students:

Cletus Ngwerume

B.S., Computer Science, Harare Institute of Technology

Cletus Takudzwa Ngwerume is a graduate student in the Master of Science in Data Science program at Michigan Technological University. His research focuses on artificial intelligence, large language models, and machine learning systems, particularly in retrieval-augmented generation (RAG), natural language processing, and AI system evaluation. Prior to his graduate studies, Cletus gained several years of experience in software engineering and enterprise system development, working on large-scale applications and data-driven platforms. He has contributed to the development of AI-powered tools for document analysis, question generation, and knowledge retrieval. In addition to his work in computing, he has experience working with electrical and power systems, with an interest in how data science and AI can be applied to energy systems, power monitoring, and data center infrastructure. His current work explores improving retrieval efficiency and performance in AI systems using modern embedding models and vector databases.

Prince Tawiah

B.S., Telecommunications Engineering, Kwame Nkrumah University of Science and Technology

Prince Tawiah is a Master’s student in Data Science at Michigan Technological University. His work focuses on machine learning, energy systems analytics, and large scale data modeling. His research studies energy demand patterns in AI data centers and the computational infrastructure supporting large scale machine learning. Before graduate school, Prince worked as flight technician at Zipline International - Ghana, where he supported drone logistics operations with maintenance, operational analytics and data driven decision tools. His research interests include AI infrastructure energy modeling, machine learning systems analysis, applied data science for large scale computing systems and autonomous transportation.

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