University of Dayton

What you'll study

The programme combines rigorous mathematical theory with practical computational techniques. You will take core courses that reinforce foundational graduate-level mathematics and choose advanced electives that reflect your interests in modelling, computation or data analysis. Instruction emphasises problem-solving, numerical simulation and communication of quantitative results.

• Core topics: real analysis and functional analysis for applications, advanced linear algebra, numerical analysis and scientific computing, and partial differential equations.
• Applied and elective topics: mathematical modelling and simulation, optimisation and control, dynamical systems, stochastic processes and applied probability, numerical methods for PDEs, computational methods in data analysis, and high-performance computing techniques.
• Project and research options: students typically complete a substantial capstone project or a thesis under faculty supervision; projects often involve interdisciplinary collaborations with the Departments of Engineering, Computer Science, Physics or industry partners and focus on modelling, numerical algorithm development, or data-driven analysis.
• Teaching and professional development: opportunities exist to serve as teaching assistants, to develop pedagogical skills, and to engage with seminars and research groups across campus.

Programme structure

The degree is offered as a coursework-plus-research programme with flexibility in full- or part-time study. Typical study consists of a set of compulsory graduate-level courses, a selection of electives, and either a supervised research thesis or an applied capstone project that demonstrates mastery of applied techniques.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related quantitative field. Successful candidates normally demonstrate strong preparation in calculus, linear algebra, ordinary and partial differential equations, and some exposure to numerical methods or programming.

• Academic background: a recognised undergraduate degree with solid performance in quantitative coursework.
• Prerequisites: coursework in multivariable calculus, linear algebra, differential equations and basic probability/statistics. Applicants who lack certain prerequisites may be admitted conditionally and asked to complete specified bridging courses.
• Application materials: official transcripts, a statement of purpose describing research and career goals, curriculum vitae, and letters of recommendation. International applicants must meet English language proficiency requirements.
• Standardised tests: requirements for tests such as the GRE vary; consult the department for current guidance.

Career prospects

Graduates with an M.S. in Applied Mathematics are prepared for quantitative roles in industry, government and academia. The programme’s focus on computation and modelling makes alumni suitable for positions that require advanced analytical skills and numerical problem-solving.

• Industry roles: data scientist, quantitative analyst, computational scientist, simulation engineer, operations research analyst, and optimisation specialist.
• Engineering and technology: modelling and simulation roles in aerospace, automotive, manufacturing and energy sectors, and software development positions that demand mathematical expertise.
• Research and further study: many graduates continue to doctoral studies in applied mathematics, computational science, engineering or statistics.
• Public sector and consulting: roles in national labs, defence research, economic modelling, and technical consulting firms.

Why study at University of Dayton

The University of Dayton offers a personalised learning environment with small class sizes and accessible faculty who actively collaborate on applied research. The mathematics department maintains strong links with engineering, computer science and industry partners, providing interdisciplinary project opportunities.

• Hands-on computational resources: access to campus computing facilities and support for numerical and data-intensive research.
• Interdisciplinary collaboration: easy access to engineering and science departments for cross-disciplinary projects and internships.
• Career support: university career services help connect students with local and national employers and support internships, co-op arrangements and job placement.
• Values-driven community: as a Catholic university with a Marianist tradition, the campus emphasises ethical application of technical skills, service, and leadership development.