Rice University

What you'll study

This programme emphasises the mathematical foundations and computational tools needed for large-scale simulation and data analysis. Core topics typically include numerical analysis, numerical linear algebra, scientific computing, and partial differential equations. Students also study algorithm design for high-performance computing, optimisation and inverse problems, and probability and statistics for computational applications.

• Numerical Analysis and Approximation: error analysis, interpolation, spectral methods and finite element ideas.
• Numerical Linear Algebra: direct and iterative solvers, preconditioning, eigenvalue computations.
• Scientific Computing & HPC: parallel algorithms, performance tuning and use of contemporary libraries and accelerators.
• PDEs and Computational Modelling: discretisation of time‑dependent and steady problems arising in physics and engineering.
• Optimisation & Data‑driven Methods: convex and nonconvex optimisation, inverse problems, and connections to machine learning.
• Electives and Interdisciplinary Options: courses from computer science, statistics, engineering or finance to tailor skills for particular applications.
• Project or Thesis: most students complete a substantial capstone project or a research thesis under faculty supervision, often involving code development, numerical experiments and written exposition.

Entry requirements

Applicants are expected to hold a bachelor’s degree with substantial preparation in mathematics, applied mathematics, computer science, engineering or a closely related quantitative discipline. Typical preparation includes coursework in calculus, linear algebra, differential equations, probability/statistics and programming.

• Official academic transcripts from all post‑secondary institutions.
• Letters of recommendation, usually two or three, attesting to quantitative ability and potential for graduate study.
• A personal statement describing background, interests in computational mathematics and proposed objectives for the degree.
• A current CV or résumé summarising relevant coursework, research, internships and programming experience.
• Proof of English language proficiency for applicants whose first language is not English (e.g. recognised tests or prior education in English), where required.

Standardised test requirements (such as the GRE) are subject to change; applicants should consult the department for current policy. Admission decisions are based on academic preparation, recommendations, fit with faculty expertise and, where relevant, research or industry experience.

Career prospects

Graduates from computational mathematics programmes enter a range of technical roles in industry, government and academia. The combination of mathematical modelling, numerical methods and software skills is valued where simulation, optimisation and large‑scale data analysis are central.

• Data scientist, machine learning engineer or quantitative analyst in finance and technology firms.
• Computational scientist or simulation engineer in energy, aerospace, automotive and materials sectors.
• Research scientist or developer in national laboratories, research institutes and high‑performance computing centres.
• Software engineer specialising in scientific or high‑performance applications.
• Continuation to doctoral study in applied mathematics, computational science or related areas.

Why study at Rice University

Rice offers a close‑knit research environment with opportunities to work directly with faculty on computational and applied mathematics problems. The university’s interdisciplinary culture facilitates collaboration with departments such as computer science, engineering and statistics, enabling projects that cross traditional boundaries.

• Access to institutional high‑performance computing resources and practical training in contemporary scientific software.
• Opportunities to collaborate with regional research centres, industry partners and nearby medical and energy sectors in Houston.
• Small cohort sizes and faculty mentorship that support both coursework and research projects, preparing graduates for technical careers or further study.