University of Tulsa

What you'll study

The PhD programme combines advanced coursework, qualifying examinations, original research and teaching experience. Early-stage study typically covers core graduate subjects such as real analysis, functional analysis, numerical analysis, partial differential equations, and applied linear algebra. Students then take specialised courses and seminars aligned with their research area, which may include computational mathematics, scientific computing, dynamical systems, mathematical biology, statistical modelling and data science, optimisation, and mathematical aspects of fluid dynamics.

Research training emphasises both theory and computation: students develop and analyse models, design numerical methods, implement algorithms, and validate results against applications. The degree culminates in a doctoral dissertation based on original research conducted under the supervision of a faculty advisor. Most students also gain pedagogical experience through teaching assistantships or by leading undergraduate courses.

• Core and elective graduate courses (examples): Advanced Real Analysis, Numerical Methods for PDEs, Spectral Methods, Applied Functional Analysis, Mathematical Modelling, Stochastic Processes.
• Qualifying exams to assess readiness for research and to transition to candidacy.
• Regular research seminars and journal clubs presenting departmental and visiting research.
• Dissertation research with regular committee reviews and a public defence.

Entry requirements

Applicants are expected to hold a strong undergraduate degree in mathematics or a closely related field; many successful applicants already hold a master’s degree. A strong background in advanced calculus, linear algebra, differential equations, and introductory real analysis is essential. Prior coursework in numerical analysis, computation, or probability/statistics is advantageous for applied tracks.

Typical application materials include official transcripts, a statement of purpose explaining research interests and fit with departmental faculty, a curriculum vitae, and letters of recommendation from academic or professional referees. International applicants must demonstrate English proficiency according to university regulations. Admissions decisions also consider evidence of research potential and the availability of faculty to supervise the proposed work.

Career prospects

Graduates of the PhD in Applied Mathematics pursue careers across academia, industry and the public sector. Common academic paths include postdoctoral fellowships and tenure-track positions in mathematics, applied mathematics, computational science and engineering departments. In industry, graduates work as quantitative analysts, data scientists, modelling and simulation experts, software developers for scientific computing, and consultants for engineering firms.

Other career destinations include national laboratories and government research agencies where advanced modelling and numerical methods are required, as well as interdisciplinary roles in biotechnology, energy, aerospace, finance and technology companies. The programme’s emphasis on both theory and computation prepares graduates to translate mathematical insights into practical solutions.

Why study at University of Tulsa

The University of Tulsa offers a personalised doctoral experience with relatively small cohorts and close faculty supervision, allowing students to develop deep working relationships with advisors and collaborators. The department supports a range of applied research areas and fosters interdisciplinary work with engineering, physics and other scientific units on campus.

Doctoral students typically have access to modern computational resources and laboratory collaborations, and are eligible for funding through teaching and research assistantships. The university’s location and professional networks also provide opportunities for applied projects and partnerships with regional industry and research organisations. Overall, TU’s PhD in Applied Mathematics is well suited to students seeking rigorous training, hands-on computational experience, and mentorship toward independent research careers.