University of Michigan

What you'll study

The programme emphasises core areas of applied mathematics such as partial differential equations, numerical analysis, scientific computing, optimisation, probability and stochastic processes, and mathematical modelling. Students typically take a mix of advanced lecture courses and computational seminars that develop both analytical techniques and software skills for large-scale simulation and data analysis.

Typical modules and topics include:

• Partial Differential Equations and Asymptotic Methods — theory and techniques for modelling continuum systems.
• Numerical Analysis and Scientific Computing — algorithms for discretisation, stability, error analysis, and implementation of solvers.
• Optimization and Control — continuous and discrete optimisation, variational methods and control theory.
• Stochastic Processes and Probability — stochastic modelling, Markov processes and applications to uncertainty quantification.
• Mathematical Modelling and Applied Projects — interdisciplinary projects linking mathematics to applications in fluid dynamics, materials, biology, climate science or finance.
• Computational Methods and High-Performance Computing — parallel algorithms, software development and use of university computing resources.

Students may follow a thesis (research) option or a coursework/non-thesis option depending on career goals; both paths encourage engagement with faculty research groups and collaborative projects across engineering, statistics, data science and domain laboratories.

Entry requirements

Applicants should hold a bachelor's degree in mathematics, applied mathematics, physics, engineering, computer science or a closely related quantitative field. A strong undergraduate record in core subjects such as multivariable calculus, linear algebra, differential equations, real analysis, and elementary probability/statistics is expected.

Typical application materials include academic transcripts, a personal statement outlining research interests and goals, two or three academic references, and a curriculum vitae. Evidence of programming ability (for example in Python, MATLAB, C/C++ or Julia) and prior coursework or projects demonstrating numerical or modelling experience strengthen an application. International applicants must demonstrate English language proficiency according to the university's standard requirements.

Career prospects

Graduates of the Master’s in Applied Mathematics pursue a wide range of careers in industry, government and academia. Common destinations include:

• Data science and machine learning roles in technology and finance firms, where mathematical modelling and statistical inference are core skills.
• Research and development positions in engineering, aerospace, energy and materials companies focusing on simulation, optimisation and computational design.
• Quantitative roles in finance and risk management that require stochastic modelling and numerical methods.
• Government and national laboratories working on applied modelling for climate, defence, and public health.
• Further academic study at the PhD level in applied mathematics, computational science, engineering or statistics.

The programme’s blend of theoretical training and computational practice prepares graduates to contribute immediately in technical teams or to continue towards research-focused careers.

Why study at University of Michigan

The University of Michigan offers a strong applied mathematics environment with access to leading faculty, diverse research groups and interdisciplinary centres. Students benefit from collaborations with engineering departments, the statistics and data science community, and research institutes such as the Michigan Institute for Data Science, which create opportunities for cross-disciplinary projects.

The Ann Arbor campus provides substantial computing resources and a supportive research culture, while career services and an extensive alumni network help graduates connect with employers in industry and research. The programme’s emphasis on both theoretical depth and practical computation makes it well suited for students aiming to solve real-world problems using mathematical methods.