Eastern Michigan University

What you'll study

The Computational Mathematics pathway builds a foundation in both rigorous mathematical theory and practical computational techniques. Core themes include numerical analysis, scientific computing, applied linear algebra, and partial differential equations, combined with training in programming and high-performance computing. Typical modules and topics you will encounter are:

• Numerical Analysis: error analysis, stability and convergence of numerical schemes for algebraic and differential problems.
• Scientific Computing and Simulation: algorithm implementation, computational experiments, and software design for large-scale simulations.
• Applied Linear Algebra: numerical methods for eigenvalue problems, large sparse systems and matrix computations.
• Partial Differential Equations and Numerical PDEs: finite difference, finite element and spectral methods for time-dependent and steady-state problems.
• Optimization and Computational Modelling: numerical optimization, constrained problems and model calibration.
• Mathematical Modelling: translating physical, biological or engineering systems into mathematical formulations and validating computational results.
• Programming and High-Performance Computing: practical use of scientific programming languages (such as Python, MATLAB, or C++), parallel computing concepts and use of computing clusters.

The programme typically offers both thesis and non-thesis options. The thesis route emphasises original research and is suitable for students preparing for doctoral study, while the non-thesis route focuses on coursework and may include a capstone project or practicum with an applied focus. Electives allow students to tailor their studies toward areas such as data science, mathematical biology, computational finance or machine learning.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering or a closely related discipline with a record of solid quantitative coursework. Key preparation includes courses in calculus, linear algebra, differential equations and introductory numerical analysis or programming.

Typical application materials include:

• Official undergraduate transcripts.
• A statement of purpose describing academic background, research or professional interests and goals.
• Letters of recommendation from academic or professional referees who can speak to quantitative ability and potential for graduate study.
• Evidence of quantitative preparation — examples are coursework in advanced calculus, linear algebra and programming experience.
• International applicants are asked to provide proof of English language proficiency when applicable.

The department may consider applicants with strong quantitative skills from other fields on a case-by-case basis and may recommend prerequisite coursework for those who need to strengthen their background.

Career prospects

Graduates with a master’s in Computational Mathematics are well placed for technically demanding roles that require strong analytical and computational expertise. Common career paths include:

• Data scientist or analyst roles in industry and tech companies, using mathematical modelling and numerical methods to extract insights from data.
• Quantitative analyst or modelling specialist in finance and insurance, building numerical models for pricing, risk and forecasting.
• Research and development positions in engineering firms, manufacturing and energy sectors where simulation and optimisation are essential.
• Positions in government laboratories and defence agencies that require expertise in simulation, numerical solution of PDEs and high-performance computing.
• Software engineering roles focused on scientific or numerical computing libraries and tools.
• Continued academic study at the doctoral level, in mathematics, applied mathematics, computational science or related disciplines.

The programme’s emphasis on applied projects, programming and access to computing resources helps graduates transition quickly into roles that demand immediate technical contribution.

Why study at Eastern Michigan University

Eastern Michigan University offers a close-knit department environment with accessible faculty who have active interests in numerical analysis, applied mathematics and computational science. Students benefit from small class sizes that enable direct mentorship and opportunities to collaborate with faculty on research projects or applied consulting.

EMU’s location in the Southeast Michigan region provides proximity to a mix of industry, technology firms and research labs, creating possibilities for internships and applied projects. The university maintains computing facilities and support for scientific programming and high-performance computing, and graduate students may find funding opportunities through teaching assistantships, research assistantships or departmental support.

Overall, the programme combines rigorous mathematical training with hands-on computational experience, preparing graduates for technical careers across industry, government and academia while offering flexibility to pursue research or applied professional paths.