Fully Funded PhD in Machine Learning, Systems Design, and Advanced Manufacturing at The University of Alabama in Huntsville
The University of Alabama in Huntsville is recruiting a motivated PhD student for a fully funded Graduate Research Assistantship in
machine learning
,
systems design
, and
advanced manufacturing
.
The project focuses on developing cutting-edge machine learning algorithms for challenges in systems and manufacturing science. The selected student will design, develop, and validate ML models using heterogeneous datasets and collaborate with experimental teams to translate those models into robust, production-ready workflows for real-world manufacturing environments.
Funding:
This is a fully funded position with a competitive monthly stipend, full tuition remission, and health insurance. Additional support for research expenses and conference travel may also be available, subject to funding and progress requirements.
Eligibility:
Applicants should have a B.S. or M.S. in Computer Science, Electrical Engineering, Applied Mathematics, Materials Science, Cybersecurity, Mechanical Engineering, or a closely related field, with a minimum undergraduate GPA of 3.0 or equivalent. Preferred backgrounds include statistics, numerical methods, optimization, peer-reviewed publications, familiarity with materials science (such as polymers or metals), and strong machine learning experience.
Supervisor:
Dr. Cheng Chen, Assistant Professor in Industrial & Systems Engineering at The University of Alabama in Huntsville, is leading the Integrated Computing Lab (ICL Lab).
Application:
Interested candidates should email a CV and unofficial transcripts to [email protected]. Shortlisted applicants will be asked to complete a questionnaire and a written statement of research interests. Applications are reviewed on a rolling basis until the position is filled.
Institution:
The University of Alabama in Huntsville is a research-intensive university with strong ties to government, aerospace, defense, and advanced manufacturing, offering a strong environment for students interested in applied machine learning and manufacturing research.
