University of Texas

What you'll study

The programme combines rigorous mathematical foundations with practical computational methods. Core topics commonly include real and complex analysis, applied linear algebra, numerical analysis, partial differential equations, optimisation, and probability and stochastic processes. Students typically take a mix of required courses and electives drawn from applied mathematics, computational science, statistics, and computer science.

Many tracks offer both thesis and non‑thesis options. The thesis track emphasises original research under faculty supervision and is suitable for students planning to continue to a PhD. The non‑thesis route focuses on a larger set of coursework and may include a substantial project or practicum in collaboration with a research centre or industry partner.

• Mathematical modelling and analysis of deterministic systems
• Numerical methods for ordinary and partial differential equations
• Scientific computing and high‑performance computing techniques
• Statistical methods, data analysis and machine learning for applied problems
• Optimisation and control theory
• Seminars and reading courses addressing current research topics

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related discipline. A strong background in calculus, linear algebra, differential equations and introductory real analysis is normally required. Prior coursework or experience in numerical methods and programming is advantageous.

Typical application materials include an official transcript, a personal statement outlining academic and research interests, and letters of recommendation from academic or professional referees. International applicants must satisfy the university’s English language proficiency requirements. Some applicants may be asked to demonstrate quantitative preparation; standardised test policies (where used) vary by department and entry cycle.

Career prospects

Graduates of an applied mathematics master’s programme pursue a range of careers across industry, government and academia. Common roles include quantitative analyst, data scientist, computational scientist, simulation and modelling engineer, operations researcher, and software developer for scientific applications. Employers are found in finance, technology, energy, aerospace, manufacturing, healthcare and defence, as well as national laboratories and research institutes.

The degree also prepares students for doctoral studies in applied mathematics, computational science, engineering and related fields. The programme’s emphasis on computational skills and interdisciplinary collaboration is particularly valued by employers seeking staff able to translate mathematical models into robust software and data‑driven solutions.

Why study at University of Texas

The University of Texas offers access to active research groups in numerical analysis, partial differential equations, optimisation, stochastic modelling and scientific computing, and benefits from close connections with engineering, computer science and statistics departments. Students can collaborate with large research facilities and centres, gaining exposure to high‑performance computing resources and interdisciplinary projects.

Located in a vibrant academic and technology ecosystem, the university provides strong industry links and internship opportunities. The department’s faculty are engaged in applied collaborations with industry and government, creating pathways for project work and employment. The programme also gives students access to seminars, workshops and a broad alumni network that supports both academic and professional advancement.