Kent State University

What you'll study

This programme combines rigorous mathematical theory with practical computational techniques. Students take core courses in numerical analysis, numerical linear algebra and methods for partial differential equations, alongside coursework in scientific computing, optimisation and approximation theory. The curriculum emphasises algorithm development, error analysis, stability, and the implementation of numerical methods on modern computing architectures.

Typical modules and topics include:

• Numerical Analysis: interpolation, numerical integration, root-finding and error propagation.
• Numerical Linear Algebra: direct and iterative solvers, preconditioning and eigenvalue computations.
• Computational Methods for PDEs: finite difference, finite element and spectral methods for time-dependent and steady problems.
• Scientific Computing and Programming: algorithm implementation in high-level and compiled languages, performance considerations and parallel computing concepts.
• Optimisation and Inverse Problems: deterministic and constrained optimisation techniques used in parameter estimation and control.
• Probability and Stochastic Methods: probabilistic models, Monte Carlo methods and uncertainty quantification (often offered as electives).
• Computational Modelling and Applications: projects or seminars applying computational mathematics to fluid dynamics, materials modelling, data assimilation, image processing or finance.

Students may choose between a thesis route, which includes original research under faculty supervision, and a non-thesis option, which typically requires a capstone project or comprehensive examination. Electives allow interdisciplinary training with computer science, engineering, physics or statistics.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics or a closely related discipline (such as applied mathematics, engineering, physics or computer science) with substantial coursework in calculus, linear algebra and differential equations. Prior coursework or experience in numerical methods and programming is highly recommended.

Typical application materials include official academic transcripts, a statement of purpose describing research or career goals, and two or three letters of recommendation from academic or professional referees. International applicants must demonstrate English language proficiency through accepted tests unless exempt. Admissions decisions are based on the overall strength of preparation and fit with departmental strengths; some applicants may be required to make up specific undergraduate prerequisites.

Career prospects

Graduates of the computational mathematics track are prepared for a wide range of technical and research-oriented careers. Common career paths include roles as computational scientists, numerical analysts, quantitative analysts, data scientists, and software engineers specialising in scientific applications. Employers span sectors such as aerospace and defence, energy and manufacturing, finance, healthcare and biotech, government laboratories and research institutes.

The degree also provides strong preparation for further study toward a PhD in mathematics, applied mathematics, computational science or related fields, and for academic careers where expertise in both analysis and computation is required.

Why study at Kent State University

Kent State University offers a university environment with accessible faculty who are active in research areas related to computational and applied mathematics, enabling close mentorship and opportunities for student involvement in projects. The department supports hands-on learning through computing facilities and encourages interdisciplinary collaboration across engineering, computer science and physical sciences.

Students benefit from a balance of theoretical training and applied experience, with options to work on industry-relevant projects or pursue original research. The programme’s flexible structure—thesis or non-thesis pathways and elective choices—lets students tailor their studies to academic or professional goals while developing the computational skills sought by employers and doctoral programmes alike.