Boise State University

What you'll study

The Computational Mathematics pathway emphasises numerical methods, algorithm development and the use of high-performance computing to solve problems from science and engineering. Core topics typically include numerical analysis, numerical linear algebra, computational methods for partial differential equations, scientific computing and numerical optimisation. Students also study complementary areas such as advanced calculus, applied differential equations, probability and statistics for modelling, and computational linear algebra.

Programme structure is flexible and may be completed through a combination of coursework and a research or project thesis. Typical modules and coursework you can expect are:

• Numerical Analysis — error analysis, convergence, stability of algorithms.
• Numerical Linear Algebra — direct and iterative solvers, eigenproblems, preconditioning.
• Computational Methods for PDEs — finite difference, finite element and spectral methods for time-dependent and steady problems.
• Scientific Computing and High‑Performance Computing — parallel programming, performance considerations and use of HPC resources.
• Numerical Optimisation — constrained and unconstrained optimisation, algorithms for large-scale problems.
• Mathematical Modelling and Simulation — translating real systems into computational models and validating results.
• Programming for Computational Mathematics — practical work using languages and tools such as Python, MATLAB and C/C++ and relevant libraries.

Students typically complete advanced elective modules aligned with faculty research interests and may undertake an independent research project or thesis that applies computational techniques to problems from fluid dynamics, materials, biology, finance or data-driven modelling.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related discipline, with evidence of strong quantitative preparation. Required background usually includes multivariable calculus, linear algebra, differential equations and some programming experience. Coursework in real analysis, probability/statistics and numerical methods is highly desirable.

Admissions decisions consider undergraduate transcripts, letters of recommendation, a statement of purpose outlining research or career goals, and relevant research or project experience. International applicants must demonstrate English language proficiency through an accepted test or equivalent. Some applicants without a formal mathematics degree but with substantial quantitative and programming experience may be considered, sometimes with the requirement to complete prerequisite courses.

Career prospects

Graduates with a computational mathematics master’s are well placed for roles that require advanced quantitative and computational skills. Common career paths include:

• Data scientist or machine learning engineer in technology and industry
• Quantitative analyst and modeller in finance or risk management
• Computational scientist or software developer for scientific applications
• Research scientist or analyst in government labs and national research centres
• Engineering analyst and simulation specialist in aerospace, energy and manufacturing
• Further study at the doctoral level for an academic or research career

The programme’s combination of theory, software practice and project work also supports transitions into interdisciplinary teams and roles that require translating mathematical models into production-ready code.

Why study at Boise State University

Boise State’s Department of Mathematical and Statistical Sciences offers a supportive, research-active environment with faculty expertise in applied and computational mathematics. Students benefit from close faculty supervision, opportunities to collaborate across engineering, computer science and the physical sciences, and access to campus computing resources and regional HPC facilities.

The university’s location provides connections with a growing regional tech sector and research organisations, which can facilitate internships and applied projects. Graduate teaching and research assistantships are commonly available to qualified students, providing financial support and hands-on experience in teaching or research. The programme’s emphasis on applied computation and real projects prepares graduates for immediate impact in industry, government and academic research.