Georgia Institute of Technology

What you'll study

The PhD programme centres on original research in core and emerging areas of industrial and systems engineering. Students combine advanced coursework with intensive dissertation work under the supervision of a faculty advisor. Program elements include:

• Core foundations: advanced optimisation, stochastic processes, probability and statistics for Engineers, simulation, and mathematical modelling.
• Specialist topics: supply chain and logistics engineering, manufacturing systems, healthcare systems engineering, human factors and ergonomics, reliability and maintenance, systems design and complexity, and energy systems.
• Data and computing: machine learning and analytics for operations, large-scale computational optimisation, simulation-based methods, and experiment design.
• Research training: seminars, methods courses in research design, teaching practicum, and professional development workshops on grant writing and presenting research.
• Milestones and structure: a combination of coursework credits, qualifying/qualitative exams or prospectus defence, proposal defence, and a doctoral dissertation based on novel research. Most students participate as research or teaching assistants and engage in interdisciplinary projects with other departments and research centres.

Entry requirements

Admission is selective and based on demonstrated research potential and fit with faculty expertise. Typical requirements include:

• Academic background: a bachelor’s degree in engineering, mathematics, statistics, computer science, operations research or a closely related field. A relevant master’s degree strengthens preparation but is not always required.
• Academic record: strong prior coursework in calculus, linear algebra, probability and statistics, and at least some coursework in optimisation, modelling or programming.
• Application materials: a detailed CV, statement of purpose describing research interests and potential faculty mentors, academic transcripts, and at least three letters of recommendation from academic or research supervisors.
• Standardised tests and English language: if required by the graduate admissions office, standardised test scores and proof of English proficiency for non-native speakers should be provided. Competitive quantitative test results and prior research experience strengthen an application.
• Research fit: successful applicants typically identify faculty whose research aligns with their interests and articulate a clear plan for doctoral research.

Career prospects

Graduates of the PhD programme pursue a wide range of high-impact careers. Typical paths include:

• Academic careers: tenure-track faculty positions and postdoctoral research in universities and research institutions.
• Industry R&D and technical leadership: senior research scientist, principal engineer or director roles in manufacturing, logistics, aerospace, automotive, energy and technology companies.
• Data science and analytics: roles applying optimisation, stochastic modelling and machine learning to operational problems in finance, retail, technology and services.
• Consulting and operations strategy: strategy and analytics consulting roles addressing supply chain, process improvement and system design challenges.
• Government and national labs: research and policy positions addressing large-scale systems, infrastructure, defence and public health.

Why study at Georgia Institute of Technology

Georgia Tech’s School of Industrial and Systems Engineering is internationally recognised for its depth of expertise and breadth of research in optimisation, analytics, manufacturing, health systems and human-centred design. The institute’s location in Atlanta provides close connections to industry, startups and government organisations, enabling applied collaborations and internship opportunities.

Doctoral students benefit from a collaborative environment with access to multidisciplinary research centres, high-performance computing facilities and active seminar series. Faculty are leaders in their fields, offering mentorship across theoretical and applied domains, and the programme emphasises both rigorous quantitative training and impactful, real-world research.