Online Doctorate of Philosophy in Industrial Engineering (PhD)
Industrial Engineering
a bachelor's Requirements
Program Overview
Want to elevate your career in industrial engineering? Earn your Doctorate in Industrial Engineering at the University of Tennessee, Knoxville. Our online doctorate program equips you with advanced research skills in various areas, including analytics, deep learning, and operations research.
The Industrial Engineering PhD program offers two concentrations: Energy Science and Engineering or Engineering Management. Admission to the program requires at least an undergraduate degree with a relevant academic background.
Earn Your Doctorate in Industrial Engineering
The PhD in Industrial Engineering program is designed for working professionals interested in learning and performing research in any of the broad areas of industrial engineering. Our faculty do research in analytics, deep learning, and operations research and have a broad array of application areas, such as health care, power systems, manufacturing, supply chain, and others.
Online learners can attend classes in real time or watch recordings. After finishing the course requirements, they will work directly with their advisors on their research topics of interest. The expectation is that the final PhD thesis will consist of three research articles that are publishable in respected peer-reviewed journals.
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Program Concentrations
The PhD in Industrial Engineering offers two concentrations:
Featured Courses
Students pursuing a PhD in Industrial Engineering will have the opportunity to take courses in diverse areas. Here are an examples of just a few:
Introduction to basic research skills in Industrial Engineering, including literature review, research question identification and definition, scientific writing, paper revision, presentation, proposal development, network building, research ethics, and an overview of Industrial Engineering research methods.
Classical optimization applied to constrained and unconstrained, non-linear, multi-variable functions; search techniques; decision making under uncertainty; game theory; and dynamic programming.
An introduction to applied data science including machine learning and data mining tools. Topics include supervised and unsupervised algorithms, techniques for improving model performance, evaluation techniques and software packages for implementation.
An introduction to real and convex analysis for engineers providing groundwork for optimization and probability theory. Topics include convergence, completeness, compactness, and continuity.
