Florida International University

What you'll study

The programme blends rigorous mathematical theory with hands-on computational work. Core areas typically include numerical analysis, partial differential equations, optimization, applied probability and statistics, dynamical systems and scientific computing. Coursework trains you to formulate problems, prove results where appropriate, and implement efficient numerical algorithms.

• Core topics: real and functional analysis foundations, numerical methods for ODEs and PDEs, linear and nonlinear optimization, spectral and finite-element methods.
• Computational and applied topics: high-performance computing for scientific applications, computational linear algebra, stochastic modelling, data analysis and machine-learning methods as applied to mathematical models.
• Electives and specialisations: students can choose electives in areas such as mathematical biology, fluid dynamics, inverse problems, control theory, financial mathematics and operations research, reflecting faculty expertise and local research groups.
• Project and thesis options: the programme commonly offers both a thesis route (research-led, supervised by faculty) and a non-thesis route (capstone/project or additional coursework). The thesis option is suitable for those aiming at doctoral study or research careers; the project option focuses on applied problem solving for industry use.
• Seminars and research interaction: students are encouraged to attend departmental seminars, collaborate with research groups (for example in computational science, applied statistics or interdisciplinary laboratories) and take part in student presentations.

Entry requirements

Applicants normally hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, statistics, computer science or a closely related quantitative discipline. Admissions typically expect evidence of strong mathematical preparation, including coursework in calculus, linear algebra, differential equations and probability/statistics.

• Academic qualifications: an undergraduate degree with a competitive GPA in a relevant field.
• Prerequisite knowledge: preparation in advanced calculus/analysis, linear algebra, ordinary differential equations and introductory numerical methods; applicants missing some prerequisites may be admitted with conditional requirements.
• Application materials: official transcripts, a personal statement outlining academic and career goals, and letters of recommendation. A CV/resume outlining relevant research, projects or computing experience is recommended.
• Standardised tests and language: standardised test requirements (such as GRE) vary by intake and applicant background; international applicants must demonstrate English proficiency via recognised tests unless exempt by university policy.

Career prospects

Graduates move into a wide range of technical and analytical roles. The programme equips students for positions where strong quantitative modelling and computational skills are required.

• Data scientist, machine-learning engineer or quantitative analyst in finance, insurance and technology firms.
• Modeller or computational scientist in engineering, environmental science, energy and biotech companies.
• Operations research analyst, optimisation specialist or simulation engineer in logistics, manufacturing and defence sectors.
• Research scientist or analyst roles in government laboratories and research institutes, and a pathway to doctoral studies and academic careers.
• Software developer roles focused on scientific computing and algorithm development.

Why study at Florida International University

Florida International University is located in Miami, a diverse metropolitan region with strong industry connections across finance, healthcare, technology and environmental science. The mathematics department fosters interdisciplinary collaboration with engineering, computer science and life-science units, providing opportunities for applied projects and internships.

• Applied and computational emphasis: FIU’s programme emphasises practical computation and modelling, supported by faculty active in numerical analysis, computational science and applied probability.
• Research and industry links: proximity to local research centres and companies enables applied projects, internships and collaboration opportunities for students seeking industry experience.
• Facilities and computing resources: access to departmental computing infrastructure and university-wide high-performance computing resources supports large-scale numerical experiments and data-driven projects.
• Support for career development: university career services, faculty mentorship and seminar programmes help students prepare for technical careers or further study.