Florida International University

What you'll study

This programme emphasises numerical methods, algorithm development, high-performance computing and data-driven modelling. You will study core topics such as numerical analysis, scientific computing, parallel and distributed computing, and computational algorithms, together with applied modules in areas like computational fluid dynamics, bioinformatics modelling, computational finance or climate and Earth system modelling.

• Core modules: numerical methods for partial differential equations, scientific programming and software engineering, parallel programming and architectures, algorithms for large-scale computation.
• Data and modelling: machine learning for scientific data, uncertainty quantification, model calibration and validation, data visualisation for scientific applications.
• Applied electives: options typically include computational biology, computational materials science, computational finance, geophysical modelling and applied optimisation.
• Capstone: students normally choose between a research thesis or a culminating project that applies computational techniques to a real-world problem, often in collaboration with faculty or industry partners.

The programme is deliberately interdisciplinary: students work with faculty from computer science, mathematics, engineering and domain science departments, and have access to institutional computing resources and research labs for hands-on implementation of large-scale simulations and data analyses.

Entry requirements

Applicants are expected to hold a bachelor’s degree in a relevant quantitative discipline such as computer science, mathematics, physics, engineering, or a related science. Typical academic preparation includes coursework in calculus, linear algebra, programming and basic statistics.

• Academic transcripts: official transcripts demonstrating an undergraduate degree from an accredited institution.
• Preparatory background: prior experience with programming and numerical methods is recommended; applicants without direct preparation may be admitted conditionally and asked to complete prerequisite coursework.
• Supporting documents: a statement of purpose outlining research or professional goals, letters of recommendation, and a curriculum vitae/resume.
• English language proficiency: international applicants must demonstrate proficiency in English through an accepted test or other university-approved evidence.

Some applicants elect to provide GRE scores where required or recommended by the department; check the programme office for current testing policies. Part-time and continuing-education applicants are considered, and prior professional experience in computational roles can strengthen an application.

Career prospects

Graduates move into a range of technical roles that require strong computational and quantitative skills. Common career paths include:

• Computational scientist or modeller in industry, government laboratories and research institutes.
• Data scientist, machine learning engineer or analytics specialist in finance, health care, energy and technology sectors.
• High-performance computing engineer or systems specialist supporting large-scale simulation and data processing.
• Quantitative analyst in finance and risk modelling, and simulation engineer in engineering consultancies.
• Further academic or research careers leading to PhD study and roles in university research groups.

The programme’s applied focus and opportunities for project work with faculty and external partners help graduates demonstrate practical experience sought by employers in both private and public sectors.

Why study at Florida International University

Florida International University offers a strong interdisciplinary environment with faculty active in computational research across engineering, physical sciences and health sciences. The university’s location in the Miami metropolitan area provides proximity to a diverse set of industries — including finance, biotechnology, environmental science and technology startups — that collaborate with the university on applied projects.

Students benefit from access to institutional computing infrastructure and research labs, opportunities for collaborative projects with faculty and local partners, and flexible study options that accommodate full-time, part-time and professional students. The programme’s emphasis on both algorithmic foundations and domain applications prepares graduates to apply computational methods to real-world scientific and engineering challenges.