Massachusetts Institute of Technology

What you'll study

The programme builds on core mathematical foundations while emphasising applications and computation. Typical topics and modules include:

• Analysis and Partial Differential Equations — theory and methods for linear and nonlinear PDEs, variational methods and existence/regularity results.
• Numerical Analysis and Scientific Computing — discretisation techniques, finite element and finite volume methods, stability and convergence, high-performance computing for large-scale problems.
• Optimisation and Control — convex and nonconvex optimisation, algorithmic methods, optimal control and applications to engineering and data assimilation.
• Probability, Stochastic Processes and Statistics — stochastic modelling, diffusion processes, stochastic differential equations and connections to uncertainty quantification.
• Mathematical Modelling — modelling techniques for fluid dynamics, materials, biological systems and other applied domains; model reduction and multiscale methods.
• Computational Data Science — numerical linear algebra, machine learning foundations, inverse problems and data-driven methods for scientific problems.

Programme structure typically combines advanced coursework with a substantial research project, practicum or thesis option under the supervision of a faculty member. Students may also take elective courses across MIT departments and collaborate with research laboratories and centres to pursue interdisciplinary projects in areas such as computational science, engineering, finance or data science.

Entry requirements

Applicants are expected to hold a bachelor’s degree (or equivalent) with strong preparation in mathematics, applied mathematics, physics, engineering, computer science or a closely related field. Typical preparation includes coursework in:

• Multivariable calculus and real analysis
• Linear algebra and differential equations
• Probability and/or statistics
• Numerical methods or computational programming (Python, MATLAB, C/C++ or similar)

Admissions decisions are based on the whole application package: academic transcripts, a statement of purpose that describes research interests and background, letters of recommendation from academic or professional referees, and a CV. Relevant research or project experience is an advantage. MIT’s graduate admissions practices may change over time; applicants should consult the Department of Mathematics website for the most current instructions about tests and document submission.

Career prospects

Graduates with a Master’s in Applied Mathematics from MIT move into a wide range of careers where rigorous quantitative skills are in demand. Common trajectories include:

• Research and development roles in technology companies, scientific computing firms and engineering consultancies, focusing on simulation, modelling and algorithm development.
• Data science, machine learning and quantitative analytics positions in finance, healthcare, energy and tech sectors.
• Technical roles in national laboratories and government agencies that require modelling, optimisation and uncertainty quantification expertise.
• Continued academic research leading to doctoral study in applied mathematics, computational science, engineering or related disciplines.
• Product and engineering roles where mathematical modelling and numerical methods inform design, forecasting and decision-making.

Connections with MIT research groups, industry partners and alumni networks often assist graduates in finding internships and early-career positions that leverage their specialised training.

Why study at Massachusetts Institute of Technology

Studying applied mathematics at MIT provides access to a concentration of world-class faculty and active research groups at the intersection of mathematics, computation and engineering. The Department of Mathematics has strengths across analysis, computation and applied theory, and students benefit from interdisciplinary collaboration with engineering departments, computer science, physical sciences and dedicated research centres.

MIT’s environment emphasises hands-on computational work, exposure to large-scale scientific computing resources and opportunities to collaborate with laboratories and industry partners. For students seeking a demanding technical education with direct links to contemporary scientific and engineering problems, the programme offers a path to both research and industry careers.