University of Kansas

What you'll study

The programme emphasises the mathematical foundations of computation together with hands-on numerical methods and algorithm development. Core topics commonly studied include numerical analysis, numerical linear algebra, partial differential equations and their numerical solution, optimisation, and scientific computing. Students typically also take courses in numerical methods for ordinary differential equations, computational modelling, probability and statistics for computation, and high-performance computing techniques.

Instruction balances theory and implementation: coursework often features programming assignments and projects using languages and tools such as Python, MATLAB, C/C++, and parallel computing frameworks. The degree can be pursued through a thesis (research) option or a non-thesis (coursework and project) option, allowing students to concentrate on research-led computational work or on applied skills for professional practice.

Entry requirements

• A completed bachelor's degree in mathematics, applied mathematics, computer science, engineering or a closely related discipline from an accredited institution.
• A strong undergraduate record in mathematics, typically including coursework in calculus, linear algebra, differential equations and proof-based mathematics; prior exposure to numerical analysis or scientific computing is advantageous.
• Evidence of programming competence (for example coursework or projects using Python, MATLAB, C/C++), and familiarity with mathematical computing tools.
• Academic transcripts, a statement of purpose detailing research or career goals, and letters of recommendation from academic or professional referees.
• For applicants whose first language is not English, an approved English language qualification is required.
• Additional requirements such as GRE scores may vary; applicants should consult the department for the most current admissions guidance.

Career prospects

Graduates with a master’s in Computational Mathematics are prepared for roles that require advanced quantitative and computational skills. Typical career destinations include data scientist or analyst roles, quantitative analyst positions in finance, scientific or engineering modelling roles in industry, software development for numerical libraries, and research or technical positions in government laboratories and national research centres.

The degree also provides a strong foundation for continued study at the doctoral level in applied mathematics, computational science, data science or related fields. Many graduates move into interdisciplinary teams working on simulation, optimisation, machine learning, numerical methods for physical systems, and high-performance computing applications.

Why study at University of Kansas

The University of Kansas offers a mathematics department with active faculty research in numerical analysis, scientific computing and applied mathematics, providing opportunities for students to engage in cutting‑edge computational research. Students benefit from collaborative links across campus with engineering, physics, computer science and data science groups, enabling interdisciplinary projects and internships.

Graduate students have access to institutional computing resources and faculty-led research projects, as well as teaching and research assistantship opportunities that provide professional experience and financial support. The department’s relatively small graduate cohorts allow close mentoring by faculty and tailored study plans to suit research interests or career aims.