Florida State University

What you'll study

The programme combines core mathematical theory with practical computational techniques. Students typically take advanced coursework in numerical analysis, numerical linear algebra, scientific computing, and the numerical solution of differential equations, together with supporting courses in applied analysis, optimisation and probability/statistics. Coursework emphasises algorithmic development, error analysis and the implementation of methods for large-scale problems.

• Core topics: Numerical Analysis; Numerical Linear Algebra; Numerical Methods for Partial Differential Equations; Scientific Computing.
• Supporting and elective topics: Optimisation and Variational Methods; Computational Statistics and Data Analysis; High-Performance Computing and Parallel Algorithms; Mathematical Modelling; Dynamical Systems.
• Project or thesis: Students may choose a thesis-based route involving original research under faculty supervision, or a coursework/project route with a substantial computational project that emphasises implementation, benchmarking and application to a real problem.
• Practical training: Emphasis on programming (commonly MATLAB, Python, C/C++ or Fortran), use of scientific libraries, and exposure to high-performance computing environments for large-scale simulations.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering or a closely related field with substantial coursework in calculus, linear algebra and differential equations. Prior exposure to proof-based mathematics, numerical methods and programming is advantageous.

• Academic record: A strong undergraduate transcript demonstrating quantitative preparation.
• Application materials: Official transcripts, a statement of purpose describing research or career objectives, curriculum vitae, and letters of recommendation from academic or professional referees familiar with the applicant’s quantitative abilities.
• Standardised tests and English language: Check the department’s admissions page for current requirements regarding standardised tests and English-language proficiency examinations for international applicants; required scores may vary and some departments may consider waivers in particular cases.
• Preparatory requirements: Applicants lacking specific prerequisites may be admitted conditionally and expected to complete specified undergraduate-level recovery courses before or during the early part of the programme.

Career prospects

Graduates with a master’s concentration in computational mathematics are prepared for a broad range of technical careers. Typical roles include computational scientist, numerical analyst, data scientist, quantitative analyst, software developer for scientific applications, and engineering analyst. The mathematical and programming skills developed also provide a strong foundation for pursuing a PhD in mathematics, applied mathematics, computational science or related disciplines.

Alumni find employment in industry sectors such as finance, engineering, energy, aerospace, biotechnology and software, as well as in government research laboratories and academic institutions. The programme’s focus on high-performance computing and large-scale simulation is particularly attractive to employers tackling complex, computation-heavy problems.

Why study at Florida State University

Florida State University’s mathematics department offers a research-active environment with faculty expertise in numerical analysis, computational mathematics and applied analysis. Students benefit from opportunities to work closely with researchers on applied problems and to participate in interdisciplinary collaborations with engineering, physics and computer science.

• Research and facilities: Access to departmental expertise in scientific computing and to campus computing resources for parallel and high-performance computation supports student projects and thesis work.
• Interdisciplinary opportunities: The programme encourages collaboration across departments, enabling students to apply computational mathematics to real-world problems in engineering, physical sciences and data analytics.
• Professional development: Graduate teaching and research assistantships provide practical experience, and the university’s connections within the region support internship and employment opportunities.
• Community: Small-group seminars, reading groups and student-led activities create a supportive environment for developing advanced mathematical and computational skills.