Georgia Institute of Technology

What you'll study

The PhD programme combines advanced coursework, comprehensive examinations and sustained supervised research leading to a doctoral dissertation. Students study core topics in analysis, computation and modelling while specialising in an application area. Program elements typically include graduate-level courses, participation in seminars and research groups, and teaching or mentoring responsibilities.

• Core coursework: Advanced real and complex analysis, functional analysis, partial differential equations, numerical analysis and scientific computing, linear algebra and approximation theory.
• Complementary topics: Probability and stochastic processes, dynamical systems and bifurcation theory, optimisation and control, inverse problems, computational statistics and machine learning, mathematical biology, and high-performance numerical algorithms.
• Research training: Qualifying or preliminary examinations to assess readiness for research, directed reading and research courses, regular participation in department seminars and colloquia, and an oral dissertation proposal and defence.
• Interdisciplinary opportunities: Collaboration with engineering, computing, physics, biology and data science units across campus; access to applied projects in computational science, materials modelling, fluid dynamics, quantum information and data-driven modelling.

Entry requirements

Admission is competitive and seeks applicants with a strong foundation in undergraduate mathematics or a closely related discipline. Typical expectations include:

• Academic preparation: A bachelor’s degree with substantial mathematics content; many successful applicants hold a master’s degree. Essential undergraduate coursework usually includes real analysis, linear algebra, differential equations and programming or numerical methods.
• Research potential: Evidence of research experience or potential through a thesis, publications, project work or strong statements describing research interests.
• Supporting documents: Transcripts, a concise statement of purpose outlining research goals, academic references that can speak to mathematical ability and research potential, and a curriculum vitae. International applicants must demonstrate English proficiency as required by the institute.
• Additional information: Admissions committees evaluate the overall fit with faculty research strengths; contacting potential supervisors and highlighting alignment with specific research groups is recommended.

Career prospects

Graduates of the Applied Mathematics PhD programme move into a wide range of research-intensive careers. Common paths include:

• Academic research and teaching: Postdoctoral positions and faculty appointments in mathematics, applied mathematics, engineering and computational science departments.
• National laboratories and research institutions: Scientific staff roles addressing large-scale simulation, modelling of physical systems and method development.
• Industry and technology: Research scientist, algorithm developer and quantitative analyst roles in sectors such as software, finance, aerospace, energy, biotech and advanced manufacturing.
• Data science and machine learning: Positions applying mathematical modelling, optimisation and computational techniques to large-scale data problems in technology companies and start-ups.
• Government and policy: Analytical and modelling roles in agencies that require rigorous quantitative assessment, forecasting and simulation expertise.

Why study at Georgia Institute of Technology

Georgia Tech offers a research-intensive environment with strong emphasis on interdisciplinary collaboration between the School of Mathematics and engineering, computing and science units. Faculty in applied mathematics work on a broad spectrum of theoretical and computational problems and maintain active partnerships with campus institutes and external laboratories.

• Interdisciplinary research environment: Opportunities to work with researchers in engineering, the College of Computing, biomedical sciences and national laboratories on application-driven mathematical problems.
• Computational and experimental resources: Access to high-performance computing facilities, specialised software and research centres that support large-scale numerical simulation and data-driven research.
• Professional development: Teaching experience, seminar series, workshops and collaboration opportunities that prepare students for academic and non-academic careers.
• Location and industry connections: Based in Atlanta, the institute has strong ties to regional and national industry partners, facilitating internships, collaborative projects and career placement.