University of North Dakota

What you'll study

The programme combines core graduate-level mathematics with specialised coursework in numerical methods and scientific computing. You will study the theory and implementation of algorithms used to solve problems in differential equations, linear and nonlinear systems, optimisation and data-driven modelling.

• Core topics: advanced calculus and real analysis, graduate linear algebra, and the mathematical foundations that underpin computational methods.
• Computational modules: numerical analysis, numerical linear algebra, numerical methods for ordinary and partial differential equations, and high-performance scientific computing.
• Applied and interdisciplinary options: mathematical modelling, optimisation and control, uncertainty quantification, computational statistics, and data assimilation, often with applications to fluid dynamics, materials, geosciences, energy systems or biosciences.
• Programming and tools: practical training in scientific programming (for example MATLAB, Python, C/C++ or parallel programming paradigms), version control and use of high-performance computing resources.
• Research and project work: thesis or non-thesis options are typically available. Thesis students undertake original research under faculty supervision; non-thesis students complete a capstone project or additional coursework.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, computer science or a closely related discipline with evidence of strong quantitative preparation. Typical preparation includes undergraduate coursework in calculus through multivariable, linear algebra, differential equations, and some exposure to programming.

Required application materials typically include:

• Official academic transcripts from previous institutions.
• A statement of purpose describing academic background, research or professional interests, and reasons for pursuing the programme.
• Letters of recommendation, usually two or three, from faculty or employers who can speak to quantitative ability and potential for graduate study.
• A current CV or résumé detailing relevant coursework, projects and experience.
• Proof of English language proficiency for applicants whose first language is not English (accepted tests and minimum scores are set by the university).

Some applicants may be asked to demonstrate readiness for graduate-level mathematics through additional coursework or qualifying examinations. Specific admissions criteria, including GPA expectations and whether standardised tests are required, are provided by the university's graduate admissions office.

Career prospects

Graduates with an MS in Computational Mathematics are sought after for roles that require strong analytical and programming skills. Common career paths include:

• Computational scientist or numerical analyst in industry or national laboratories.
• Data scientist, machine learning engineer or quantitative analyst in finance, technology and consulting.
• Research and development positions in engineering firms, energy companies, aerospace and biomedical industries.
• Software developer roles focussed on scientific and technical computing.
• Further academic study: many graduates proceed to PhD programmes in applied mathematics, computational science or related fields.

Why study at University of North Dakota

The University of North Dakota offers a department with active faculty in applied and computational mathematics, providing opportunities for close mentorship and collaborative research. The programme’s connections with engineering, physics and computer science departments enable interdisciplinary projects and exposure to real-world applications.

Students benefit from access to campus computing facilities and high-performance resources, opportunities for teaching and research assistantships, and a learning environment that emphasises both theoretical rigour and practical implementation. The university’s regional partnerships with industry and government laboratories can help students find internships and applied research experiences relevant to computational careers.