Rice University

What you'll study

The programme emphasises mathematical modelling, analysis and computation across a range of application areas. Core subject areas typically include numerical analysis, partial differential equations, scientific computing, optimisation, dynamical systems and probability/stochastic methods. Elective topics allow specialisation in areas such as computational fluid dynamics, inverse problems and imaging, machine learning for scientific problems, mathematical biology and financial mathematics.

Students follow an individualised curriculum that combines rigorous theoretical coursework with substantial computational components and project work. Assessments commonly involve problem sets, programming assignments, computational projects and a substantial capstone piece — either a research thesis or a supervised project in collaboration with faculty. Courses make regular use of high‑performance computing facilities and modern software tools for scientific computation.

Entry requirements

• An undergraduate degree in mathematics, applied mathematics, physics, engineering, computer science or a closely related quantitative discipline.
• A strong foundation in calculus, linear algebra, differential equations and basic real analysis; prior exposure to numerical methods and programming is expected.
• Application materials typically include official transcripts, a personal statement describing academic and professional goals, letters of recommendation, and a curriculum vitae.
• International applicants must demonstrate English language proficiency through an accepted test unless otherwise exempt.
• Admissions decisions are based on the overall strength of the academic record, quantitative preparation, research or project experience where applicable, and the fit between the applicant's interests and faculty expertise. Specific documentation requirements and any standardised test policies are published by the university and should be checked on the programme website.

Career prospects

Graduates move into a broad range of roles that use advanced quantitative and computational skills. Common career paths include data scientist or quantitative analyst roles in finance and analytics firms; computational scientist or numerical modeller positions in engineering, energy and aerospace companies; research and development roles in technology and biotech; and technical roles at national labs and research institutes. Many students also use the master's as preparation for PhD programmes in applied mathematics, computational science, statistics or related disciplines.

Why study at Rice University

Rice offers a focused and collaborative environment with relatively small class sizes that enable close interaction with faculty. The university's location in Houston provides easy access to major industries — including energy, aerospace, healthcare and finance — creating opportunities for applied projects, internships and interdisciplinary collaboration. Faculty strengths span scientific computing, numerical analysis, optimisation, mathematical modelling and applications in biology, engineering and data science, and students benefit from access to institutional computational resources and cross‑departmental research centres.

Additionally, the department maintains links with engineering, computer science and life‑science groups at Rice, allowing students to tailor their studies to practical problems and to work on projects that bridge mathematics and application domains.