University of Chicago

What you'll study

This programme emphasises a blend of core mathematical theory and hands-on computational methods. Core topics typically include numerical analysis, scientific computing, advanced linear algebra, and the numerical solution of differential equations. Students also study complementary subjects such as optimisation, probability and stochastic processes, numerical PDEs, and data-analytic methods including elements of machine learning and statistical computing.

Instruction mixes rigorous proof-based courses with laboratory-style classes that use high-performance computing tools and software. Typical modules and components include:

• Numerical Linear Algebra and Matrix Computations
• Numerical Methods for Ordinary and Partial Differential Equations
• Scientific Computing and Parallel Algorithms
• Optimisation and Computational Convex Analysis
• Probability, Statistics and Uncertainty Quantification
• Machine Learning for Scientific Data and Inverse Problems
• Advanced topics seminars in areas such as computational geometry, multiscale modelling, and computational fluid dynamics
• Capstone project or master's thesis involving implementation, experimentation and analysis on a substantive computational problem

Students can tailor the programme through electives in the departments of Computer Science and Statistics, or through interdisciplinary projects with research groups focused on applied and computational mathematics.

Entry requirements

Applicants are expected to hold a bachelor’s degree or equivalent with substantial preparation in mathematics. Competitive candidates typically have completed coursework in multivariable calculus, real analysis, linear algebra, and differential equations; coursework or experience in numerical methods and programming is strongly recommended.

Required application materials usually include official academic transcripts, a curriculum vitae, a personal statement describing academic and research goals, and academic letters of recommendation. International applicants should demonstrate sufficient English proficiency by accepted testing or other institutional criteria. Evaluation emphasises demonstrated quantitative ability, prior coursework or projects in computational mathematics, and preparedness for graduate-level, proof-based work.

Career prospects

Graduates of a computational mathematics master’s programme pursue a range of careers that leverage advanced quantitative and computational skills. Common paths include:

• Quantitative analyst or model developer in finance and risk management
• Data scientist or machine learning engineer in technology and industry
• Scientific programmer and software engineer for high-performance and simulation codes
• Research scientist or computational scientist in national laboratories, research institutes, or industry R&D
• Roles in engineering, biotech and climate modelling requiring numerical simulation expertise
• Continuation to doctoral study (PhD) in applied mathematics, computational science, computer science, or statistics

Students often benefit from collaboration opportunities, internships and connections to nearby research labs and industry partners when preparing for these careers.

Why study at University of Chicago

The University of Chicago offers a rigorous mathematical culture combined with strong interdisciplinary connections across computer science, statistics, and physical sciences. The department emphasises deep theoretical foundations together with practical computational training, giving students the analytical tools to tackle challenging numerical problems.

Students have access to research projects, seminars and collaborative groups working on applied and computational topics, and opportunities to engage with regional research institutions and industry partners. Small class sizes and close mentorship from faculty enable tailored study plans and research supervision. The university’s career services and alumni network further support transitions into industry, national labs or doctoral study.