Dongping Du

The Department of Industrial, Manufacturing, and Systems Engineering (IMSE) at Texas Tech University is inviting applications for a Ph.D. position in Machine Learning, Artificial Intelligence, and Applied Statistics, with an expected start date of Fall 2026. The successful candidate will be jointly supervised by Associate Professor Dongping Du and Dr. Fan Bu. This opportunity is ideal for students interested in research on data-driven decision-making and intelligent systems, with applications spanning reliability, risk assessment, healthcare, manufacturing, and other data-rich domains. Texas Tech University is a Carnegie R1 institution with a strong research profile and extensive laboratory facilities in manufacturing systems, bioprinting, nanofabrication, complex systems modeling, and more. The IMSE department is known for its interdisciplinary research environment and collaboration opportunities across engineering and computer science. Research topics for this Ph.D. position include machine learning, deep learning, trustworthy and explainable AI, statistical learning, and high-dimensional data analysis. The work will involve applying these methods to real-world problems in risk assessment, reliability engineering, healthcare analytics, and manufacturing systems. Applicants should possess a master's degree (or an outstanding bachelor's degree) in Industrial Engineering, Statistics, Computer Science, Applied Mathematics, or a closely related field. Essential skills include a strong background in probability, statistics, linear algebra, and optimization, as well as programming proficiency in Python or R. Prior research experience and familiarity with machine learning tools are advantageous. The position offers tuition support and a graduate assistantship, providing financial stability and access to high-performance computing resources. The selected student will benefit from a collaborative research environment and the chance to work with leading experts in the field. Applications are reviewed on a rolling basis and will remain open until the position is filled. Interested candidates should send a CV, transcripts, and a brief statement of research interests to [email protected] and [email protected]. Early applications are encouraged.