University of Virginia

What you'll study

The programme combines rigorous theoretical courses with computational and applied projects. Core topics commonly include real analysis and advanced calculus for applications, partial differential equations, numerical analysis and scientific computing, probability and stochastic processes, and optimisation. Students typically take electives that reflect their interests, such as mathematical modelling, dynamical systems, inverse problems and data-driven methods (including machine learning and statistical modelling).

Programme structures often offer a choice between a thesis (research) option and a non‑thesis (coursework or project) option. Thesis students work with a faculty advisor on a significant original project; non‑thesis students may complete a substantial applied project or additional coursework. Practical training emphasises coding and computation (Matlab, Python, or other scientific computing environments) and access to campus high‑performance computing resources for large simulations or data analysis.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related quantitative discipline. Strong performance in undergraduate mathematics (real analysis, linear algebra, differential equations) and some computational experience are typically required.

• Academic transcripts demonstrating quantitative coursework
• Letters of recommendation (usually two or three) from academic or professional referees
• A personal statement outlining academic background, research interests and career goals
• For applicants whose first language is not English, an approved English proficiency test score is required

Standardised tests (such as the GRE) may be optional or considered at the admissions committee’s discretion; applicants should check the department’s current guidance. Relevant research, projects or industrial experience strengthen an application.

Career prospects

Graduates from an applied mathematics master’s programme pursue a wide range of careers. Typical roles include quantitative analyst, data scientist, statistical modeller, computational scientist, operations research analyst and software engineer for scientific computing. Graduates also enter research and development positions in industries such as finance, energy, biotechnology, aerospace and defence, and work in government labs or consulting firms.

Many students use the master’s as preparation for PhD study in applied mathematics, computational science or related fields; the programme’s emphasis on modelling and computation provides a strong foundation for doctoral research.

Why study at University of Virginia

The University of Virginia offers access to a strong mathematics faculty with active research in applied analysis, computation and interdisciplinary applications. Students benefit from collaborative opportunities across the School of Engineering and Applied Science, the College, and research institutes on campus, including centres with strengths in data science and computational modelling.

Facilities and resources include computing infrastructure for large‑scale simulation and data analysis and opportunities for applied projects with faculty in engineering, physics, medicine and industry partners. The department’s environment emphasises close faculty mentorship, a balance of theory and practice, and preparation for both professional and academic careers.