University of Arizona

What you'll study

The MS in Applied Mathematics emphasises mathematical modelling, numerical methods and applied analysis, combined with practical experience using modern computational tools. Typical core subjects include partial and ordinary differential equations, numerical analysis, scientific computing, mathematical modelling and applied linear algebra. Elective topics commonly offered allow specialisation in areas such as stochastic processes and probability, optimization, inverse problems, machine learning for scientific applications, and high-performance computing.

Students usually choose between a thesis option (research-led, with a written dissertation) and a non-thesis option (coursework plus a capstone project or comprehensive examination). Coursework comprises advanced lectures, applied seminars and computational labs; students have opportunities to work on interdisciplinary projects with faculty in engineering, physical sciences, biosciences and data science centres.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or another quantitatively focused discipline. A solid foundation in calculus, linear algebra, differential equations and basic probability/statistics is expected. Prior programming experience (for example in MATLAB, Python or C++) and familiarity with numerical computation are strongly recommended.

Typical application materials include official academic transcripts, a personal statement describing research or professional interests, letters of recommendation and a curriculum vitae or résumé. International applicants must demonstrate English language proficiency through an approved test or equivalent qualification. The department may request additional supporting materials or recommend preparatory coursework for candidates with non-standard backgrounds.

Career prospects

Graduates of the programme move into roles that require strong quantitative and computational ability. Common career paths include positions as data scientists, quantitative analysts, computational scientists, modelling and simulation engineers, software developers for scientific applications, and roles in optimisation and risk analysis. Employers range across industry sectors such as aerospace and defence, finance and fintech, engineering firms, energy and environmental modelling companies, technology companies and national laboratories.

The degree also prepares students for further academic study; many alumni continue to PhD programmes in applied mathematics, computational science, engineering or related disciplines. The programme’s combination of theory and hands-on computing experience is particularly useful for applicants aiming to join research groups or pursue interdisciplinary projects.

Why study at University of Arizona

The University of Arizona offers a strong applied mathematics environment with active research groups in numerical analysis, computational science, inverse problems, and mathematical modelling. Students benefit from interdisciplinary collaboration across Colleges—particularly engineering, astronomy, geosciences and biosciences—reflecting Tucson’s broad research strengths.

The department provides access to modern computing resources and opportunities to work with faculty on grant-funded research projects and industry partnerships. Practical training, seminars and guest lectures connect students with regional employers, national laboratories and research centres, helping to bridge academic study and professional careers.

Faculty mentorship, flexible options between thesis and non-thesis tracks, and opportunities for cross-disciplinary coursework make the University of Arizona a suitable place for students seeking rigorous mathematical training applied to contemporary scientific and engineering challenges.