University of Texas

What you'll study

This programme combines core mathematical theory with intensive training in numerical and computational techniques. Typical topics include numerical analysis, numerical linear algebra, scientific computing, numerical solutions of partial differential equations, optimisation and inverse problems, stochastic numerical methods, and algorithms for large-scale computation. Coursework also emphasises practical skills: high-performance computing, parallel programming, software development for scientific applications, and applied modelling in areas such as fluid dynamics, materials, and data-driven simulation.

Structure options commonly include a coursework (non-thesis) track and a thesis track. Students follow a planned set of graduate courses and complete a capstone project or master's thesis under faculty supervision. Electives from allied departments—computer science, engineering, statistics or computational biology—allow specialisation. Seminars and reading courses provide exposure to current research and opportunities to work with research groups on applied computational problems.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree in mathematics, applied mathematics, computer science, engineering, physics or a closely related discipline. Strong preparation in calculus, real analysis, linear algebra, ordinary differential equations and numerical methods is expected. Demonstrable programming experience (for example in C/C++, Python, MATLAB or Julia) and familiarity with basic algorithms are advantageous.

Typical application materials include official transcripts, letters of recommendation, a statement of purpose describing research or professional objectives, and a curriculum vitae. International applicants must meet the University of Texas’s English language proficiency requirements. Some applicants may be invited to interview or to provide examples of prior computational or research work.

Career prospects

Graduates with a Master’s in Computational Mathematics are in demand across sectors that rely on advanced numerical modelling and data-intensive computation. Common career paths include roles as computational scientists, numerical analysts, data scientists, quantitative analysts in finance, software engineers specialising in scientific computing, and simulation or modelling engineers in energy, aerospace, and manufacturing. The programme also provides a strong foundation for students who choose to pursue doctoral research in applied mathematics, computational science or related fields.

Why study at University of Texas

The University of Texas offers a large mathematics department with faculty active in numerical analysis, scientific computing and interdisciplinary computational research. Students benefit from access to institutional high-performance computing resources and collaboration opportunities with computer science, engineering and domain research centres. The university’s connections with local and national research labs and industries provide avenues for internships, projects and employment. Additionally, the campus environment supports seminars, research groups and applied projects that bridge theory and practical computational work.