Systems & Industrial Engineering
Doctor of Philosophy
Quick Facts
Top 1%
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- Center for World University Rankings, 2024
1st
Systems Engineering Program
in the World
The PhD in Systems & Industrial Engineering (SIE) is designed for scholars and professionals who want to expand their expertise, conduct high-impact research, and shape the future of complex systems. Established as the world’s first Systems Engineering program, Arizona combines a legacy of leadership with a forward-looking curriculum that emphasizes both advanced theory and real-world application.
Students build a strong foundation in probability and statistics, optimization, and systems engineering while developing the ability to identify research gaps, define new directions, and address real-world challenges faced by industry and government. The program’s method- and application-agnostic approach encourages interdisciplinary collaboration and fosters innovative problem-solving across domains such as healthcare, energy, defense, manufacturing, and data analytics.
Emphasizing systems thinking, modeling, and lifecycle management, the program prepares graduates to advance systems engineering practice and contribute to the evolution of the discipline. Offered fully online, the SIE PhD enables working professionals to pursue doctoral study without pausing their careers, blending professional experience with advanced scholarship.
Graduates emerge with the skills to innovate, inform policy, and lead in academia, industry, and government, advancing systems and industrial engineering across disciplines and industries.
*The candidate must have attained a Bachelor's in Systems Engineering, Industrial Engineering, or a related field to be considered for admission to the Doctoral program.
*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.
The following courses can be selected in three different core focus areas: Probability & Statistics, Optimization, Systems Engineering:
Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes. This course can be selected with the Probability & Statistics core focus area.
Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments. This course can be selected with the Probability & Statistics core focus area.
Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods. This course can be selected with the Optimization core focus area.
This course focuses on the study and application of data structures that are critical for solving complex computational problems. This course can be selected with the Optimization core focus area.
Process and tools for systems engineering of large-scale, complex systems: requirements, performance measures, concept exploration, multi-criteria tradeoff studies, life cycle models, system modeling, etc. Graduate-level requirements include extensive sensitivity analysis of their final projects. This course can be selected with the Systems Engineering core focus area.
An intensive study of continuous and discrete linear systems from the state-space viewpoint, including criteria for observability, controllability, and minimal realizations; and optionally, aspects of optimal control, state feedback, and observer theory. This course can be selected with the Systems Engineering core focus area.
Outcomes
Skills
Earning your Doctor of Philosophy in Systems & Industrial Engineering will build core skills, including:
- Conducting research
- Professional report writing
- Research claim assessment
- Optimization techniques
- Project management
- Statistical analysis
- Complex systems modeling & design
- Financial modeling
- Linear systems
- Statistics & stochastic modeling
Potential Career Paths
Graduates of the Systems & Industrial Engineering (PhD) program will be prepared to pursue careers in the following fields:
