Brandeis University

What you'll study

The programme emphasises the development and application of numerical and algorithmic techniques to solve mathematical models arising in science, engineering and data-driven fields. Core topics commonly include numerical analysis, numerical linear algebra, scientific computing, numerical solution of partial differential equations, and optimisation. Students also study complementary areas such as probability and stochastic processes, computational statistics, machine learning methods, and algorithm design.

Teaching is delivered through a mixture of lectures, problem sessions and hands-on computing labs. Most students complete a substantial culminating experience: either a research thesis under faculty supervision or a capstone project that applies computational methods to real-world data or modelling challenges. Electives are available across the Mathematics Department and related departments such as Computer Science, Physics, and Biology, allowing interdisciplinary specialisation (for example, computational biology, scientific machine learning, or computational finance).

• Numerical Analysis and Scientific Computing
• Numerical Linear Algebra
• Numerical Methods for PDEs
• Optimisation and Inverse Problems
• Probabilistic Modelling and Stochastic Simulation
• High-Performance Computing and Parallel Algorithms
• Machine Learning and Data Analysis for Scientific Applications
• Graduate Seminar and Capstone or Thesis

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering, physics or a closely related quantitative discipline. Successful candidates will normally have completed undergraduate coursework in calculus, linear algebra, differential equations and an introduction to proofs; background in numerical methods and programming is strongly recommended.

Typical application materials include official transcripts, a statement of purpose outlining research and career goals, two or three academic or professional letters of recommendation, and a résumé or CV. For applicants whose first language is not English, proof of English proficiency is required. The department considers applicants holistically and may recommend prerequisite or bridge coursework for those who show strong potential but lack specific prerequisites.

Career prospects

Graduates with a computational mathematics master’s develop skills that are highly sought after in both industry and academia. Career paths include data scientist or analyst, quantitative analyst in finance, computational scientist in engineering or physical sciences, software engineer for scientific computing, and research scientist in industry or national laboratories. The degree also prepares students for doctoral study in applied mathematics, computer science or related fields.

Alumni commonly find roles in technology and software companies, biotech and pharmaceuticals, financial services, energy and engineering firms, as well as research labs and government agencies. The programme’s emphasis on practical computing, modelling and interdisciplinary collaboration helps graduates transition to applied research teams and product-oriented roles that require both mathematical rigour and software development capability.

Why study at Brandeis University

Brandeis offers a close-knit department with accessible faculty who have active research programmes in applied and computational mathematics. Small class sizes encourage personalised mentorship and opportunities to join faculty-led research projects. The university’s location near Boston and Cambridge provides easy access to a dense network of tech companies, research institutes and biotech firms, creating opportunities for internships, collaborations and industry contacts.

Students benefit from interdisciplinary links between Mathematics, Computer Science, Physics and the Data Science initiatives on campus, as well as access to campus computing resources and high-performance facilities. Career services and a campus culture that supports undergraduate and graduate research help students translate their technical training into employment or continued study.