Virginia Commonwealth University

What you'll study

This programme emphasises numerical analysis, scientific computing and algorithmic techniques for solving mathematical models arising in engineering, physical sciences and data science. Students take a mix of core and elective courses that cover both the theoretical foundations and practical implementation of computational methods.

• Core topics: numerical analysis, numerical linear algebra, and advanced methods for ordinary and partial differential equations.
• Computational and applied topics: finite element and finite difference methods, spectral methods, computational fluid dynamics, optimisation and inverse problems, and stochastic modelling.
• Computing and software: scientific programming (typically in languages such as Python, MATLAB, C/C++ or Fortran), high-performance computing and parallel algorithms, numerical libraries and reproducible computational workflows.
• Data-oriented modules: computational statistics, machine learning for scientific data, and data assimilation techniques where offered.
• Seminar and research: participation in research seminars and the option to undertake a supervised master's thesis or a terminal project/course-based capstone, depending on the chosen track.

The degree is typically structured as a sequence of graduate-level courses complemented by a seminar and a culminating experience — either a research thesis for students aiming for doctoral work or a project/capstone for those targeting industry careers. Elective choices allow interdisciplinary work with engineering, computer science or related departments.

Entry requirements

Applicants normally hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related discipline. Typical academic preparation includes multivariable calculus, linear algebra, differential equations, and some exposure to proof-based mathematics (real analysis) and scientific programming.

• Academic transcripts: official transcripts demonstrating satisfactory undergraduate performance in quantitative subjects.
• Supporting documents: a statement of purpose outlining research or career goals, and two to three letters of recommendation from academic or professional referees.
• Standardised tests: check current departmental guidance; some applicants may be required to submit English language proficiency scores if their prior education was not in English.
• Prerequisites: applicants lacking specific mathematical or programming preparation may be admitted conditionally and asked to complete prerequisite coursework.

Career prospects

Graduates are equipped for roles that require strong quantitative modelling and computational skills. Typical career paths include:

• Computational scientist or numerical analyst in government laboratories, research institutes and industry R&D.
• Data scientist, quantitative analyst or modelling specialist in finance, insurance and consulting.
• Scientific software developer, HPC engineer or algorithm engineer in technology companies and startups.
• Applied mathematician working on interdisciplinary projects with engineering, biomedical, environmental and energy sectors.
• Continuation to doctoral programmes in applied mathematics, computational science, engineering or closely related fields for those pursuing academic research careers.

Why study at Virginia Commonwealth University

VCU offers a campus environment that supports interdisciplinary collaboration between mathematics, engineering, computer science and the biomedical sciences. The Department of Mathematics and Applied Mathematics provides access to faculty active in numerical analysis, inverse problems, optimisation and computational modelling, allowing students to join ongoing research projects.

• Research opportunities: students can engage with faculty-led projects and seminars that bridge theory and applications in computational mathematics.
• Facilities: access to institutional computing resources and opportunities to gain experience with parallel computing and modern scientific software stacks.
• Location and connections: being in Richmond, students benefit from regional industry, government and medical research partnerships that can lead to internships and applied projects.
• Flexible pathways: options for a thesis or non-thesis project allow students to tailor the degree toward research or professional practice.

Overall, the programme is designed to produce graduates who can translate mathematical theory into robust computational tools for real-world problems.