North Carolina State University

What you'll study

The programme combines theoretical and computational courses to build expertise in modelling, analysis and numerical simulation. Students typically follow a 30–36 credit structure offered as a thesis (research) or non-thesis (coursework/project) option.

• Core topics: advanced linear algebra, real and complex analysis, partial differential equations, and probability/stochastic processes.
• Computational and numerical methods: numerical analysis, scientific computing, finite element and finite difference methods, high performance computing and numerical linear algebra.
• Applied specialisms: dynamical systems and control, mathematical optimisation, data-driven modelling, inverse problems, and computational biology or fluid dynamics depending on faculty offerings.
• Research and project work: thesis students undertake supervised research culminating in a written thesis; coursework students complete a substantial applied project or internship with departmental approval.
• Electives and cross-disciplinary courses: many students take electives in statistics, computer science, engineering or the Institute for Advanced Analytics to tailor the degree towards data science, computational engineering or other applied fields.

Entry requirements

Applicants should hold a bachelor's degree in mathematics, applied mathematics, engineering, physics or a closely related quantitative field. Admissions typically expect strong preparation in calculus, linear algebra, differential equations, and basic numerical methods. Evidence of programming competence (for example in Python, MATLAB or C++) is beneficial.

• Required materials commonly include official transcripts, a statement of purpose describing research or professional goals, and letters of recommendation.
• Standardised test policies can vary; applicants should consult the department for the current stance on GRE scores.
• International applicants must demonstrate English language proficiency through recognised tests unless exempt; check the department for acceptable proof and minimums.
• Competitive applicants typically show strong quantitative performance and, for research tracks, prior experience in independent projects or undergraduate research.

Career prospects

Graduates move into a broad range of roles in industry, government and academia. The programme’s combination of analysis and computation prepares students for technical careers where mathematical modelling and quantitative computing are central.

• Quantitative analyst, data scientist or machine learning engineer in finance, tech and healthcare.
• Computational scientist, simulation engineer or numerical analyst in aerospace, energy and manufacturing companies.
• Research scientist or applied mathematician in national laboratories, government agencies and R&D departments.
• Continuation to doctoral study in applied mathematics, computational science or related interdisciplinary fields.

Why study at North Carolina State University

North Carolina State offers a strong applied mathematics environment with close links to engineering, statistics, computer science and the Institute for Advanced Analytics. The university’s location in the Research Triangle provides access to industry partners, research laboratories and internship opportunities.

• Interdisciplinary collaboration: students can take courses and work on projects with researchers across engineering, life sciences and computing departments.
• Research and computational resources: access to high-performance computing facilities and active research groups in numerical analysis, optimisation and applied modelling.
• Professional connections: proximity to Research Triangle Park and strong ties with regional employers support internships and applied projects.
• Flexible paths: options for thesis or non-thesis tracks allow students to choose between research preparation and professionally oriented training.