Fairleigh Dickinson University

What you'll study

The programme combines rigorous mathematical theory with practical computational techniques. Core topics typically include numerical analysis, numerical linear algebra, scientific computing, numerical solution of differential equations, and numerical optimisation. You will also study foundational subjects such as real and complex analysis, mathematical modelling and probability and statistics as they apply to computational problems.

Practical training emphasises algorithm implementation and validation using contemporary tools and languages (for example MATLAB, Python and relevant scientific libraries). Most students complete advanced elective modules in areas such as high-performance computing, computational fluid dynamics, data assimilation, machine learning for scientific computing, or stochastic simulation.

Students often have the option to complete a substantial capstone project or a thesis that applies computational methods to a concrete problem in engineering, physics, finance, biology or another applied area. Projects may be carried out in partnership with faculty research groups or external industry partners.

Entry requirements

Applicants should hold a recognised bachelor’s degree in mathematics, applied mathematics, statistics, computer science, engineering or a closely related discipline. Successful applicants typically demonstrate competence in multivariable calculus, linear algebra, differential equations and basic programming. A strong academic transcript is important; additional supporting materials usually include a personal statement outlining goals and relevant experience, and at least one academic or professional reference.

International applicants must meet the university’s English language requirements and provide certified transcripts. Where an applicant’s background shows gaps in required mathematical or programming preparation, the department may recommend or require bridge courses before or during the programme.

Career prospects

Graduates are prepared for technical and analytical roles across industry and research. Common career paths include:

• Computational scientist or numerical analyst, developing and validating simulation codes for engineering and scientific problems.
• Data scientist, quantitative analyst or statistician, applying numerical methods and data-driven models to business, finance and technology problems.
• Software engineer or developer focused on scientific or technical computing libraries and applications.
• Operations researcher, optimisation specialist or modelling engineer working on logistics, energy systems or manufacturing problems.
• Continued academic study at the doctoral level in applied mathematics, computational science or related disciplines.

Why study at Fairleigh Dickinson University

Fairleigh Dickinson offers a personalised learning environment with small classes and faculty who are active in applied and computational research. The university’s proximity to major metropolitan centres provides access to internship and collaboration opportunities with industry, finance and technology organisations. Students benefit from computing facilities, interdisciplinary collaboration across science and engineering, and career services that help bridge academic study with professional placement.

The programme’s balance of theory, hands-on programming and project work is designed to produce graduates who can both formulate mathematical models and implement efficient numerical solutions — a combination valued by employers in science, engineering and data-driven industries.