Online Doctor of Philosophy in Engineering Management

Program Description

The online Ph.D. in Engineering Management is designed for professionals seeking to advance their careers in engineering and technical management roles. Throughout the program, students engage in rigorous coursework and research, gaining a deep understanding of engineering principles and their application in a management context. Graduates are equipped with the skills to effectively lead engineering teams, oversee complex projects, implement innovative strategies, and shape the future of engineering management.

Doctoral candidates are expected to undertake rigorous research that contributes original insights to the theory or practice of managing complex engineering systems. Their dissertations typically address strategic, organizational, or operational challenges at the intersection of engineering, technology, and management. Research areas may include innovation management, decision analytics, project and risk management, sustainability, or technology policy. Graduates are well-equipped for academic and research careers, as well as leadership roles in industry, government, and consulting, where they apply their expertise to drive organizational performance and inform evidence-based decision-making.

Curriculum

The program consists of a minimum of 60 credit hours divided into two stages: the classroom phase (24 credit hours) and the research phase (36 credit hours). During the research phase, the student writes and defends research on a topic related to Engineering Management. The topic is selected by the student and approved by the research advising committee.

Learn More About the Courses

SEAS 8210 AI & Tech Management: Evaluating AI technologies, managing implementation, mitigating risks, and leveraging AI insights for strategic decision making. Exploration of innovation, ethics, and AI-driven competitive advantage in technology management. (4 credit hours)

SEAS 8220 Data Science: Designing data collection methods, managing datasets, and applying analytical techniques to extract insights. Tools and strategies for effective data-driven decision making in diverse fields. (4 credit hours)

SEAS 8510 Analytical Methods for Machine Learning: Python foundations for artificial intelligence and machine learning. Mathematical tools for building machine learning algorithms and linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics. (4 credit hours)

SEAS 8601 Resilient Enterprise Security Architecture: The intersection of threat intelligence, vulnerability management, and secure design. Using architectural thinking and frameworks such as MITRE and AI-TRiSM to build resilient, requirement-driven security models for specialized technologies. (4 credit hours)

SEAS 8225 Computational Modeling: Techniques for modeling and simulating complex systems, covering data generation, Monte Carlo methods, system dynamics, agent-based and discrete event simulation. Optimization techniques and forecasting methods for predictive modeling. (4 credit hours)

SEAS 8298 Dissertation Development for Engineering Management: Aims and purposes of the praxis in Engineering Management. Development of praxis research strategies; developing Chapters 1 & 2 of the praxis; overview of research methods and required data. Restricted to students who have completed all required coursework for the Doctor of Engineering. (4 credit hours)

SEAS 8287 Dissertation Research for Doctor of Philosophy in Engineering Management: Independent applied research in engineering management culminating in the final praxis report and final examination for the degree of Doctor of Engineering. May be repeated for credit. Restricted to students in the PhD in the field of engineering management program. (36 credit hours)