Syracuse University

What you'll study

The curriculum emphasises mathematical modelling, numerical methods and computational tools used to analyse problems arising in the physical sciences, engineering and data-driven industries. Students take core courses in advanced linear algebra, real analysis for applied mathematics, numerical analysis and partial differential equations, together with coursework in probability and stochastic processes, optimisation and scientific computing.

Typical elective and specialised topics include:

• Numerical methods for ordinary and partial differential equations
• Computational linear algebra and high-performance computing for scientific applications
• Mathematical modelling and asymptotic analysis
• Stochastic modelling, time series and applied probability
• Machine learning, data science methods and statistical learning
• Control theory, dynamical systems and optimisation

The programme usually offers flexibility between a course-only route and a research-oriented option. Students can complete a capstone project or practicum with an industry partner, undertake an applied thesis under faculty supervision, or pursue internship opportunities coordinated through university career services. Computational labs and access to campus high-performance computing support numerical experimentation and large-scale data work.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or another quantitative discipline. A solid foundation in calculus, linear algebra, differential equations and basic probability/statistics is essential. Prior exposure to programming (e.g. Python, MATLAB or C++) and numerical methods is strongly recommended.

Typical application components include academic transcripts, a statement of purpose outlining research and career goals, letters of recommendation, and a résumé or CV. International applicants must demonstrate English language proficiency through an approved test or an equivalent qualification. Specific documentation requirements and any standardised-test policies are detailed on the university’s admissions pages and may vary by applicant background.

Career prospects

Graduates with an MS in Applied Mathematics are well positioned for roles that require quantitative modelling, computational skills and problem solving. Common career paths include:

• Data scientist or machine learning engineer in technology and analytics firms
• Quantitative analyst or risk modeller in finance and insurance
• Research scientist or computational modeller in engineering, energy and manufacturing
• Operations research analyst or optimisation specialist for logistics and supply chain
• Software developer focusing on scientific computing or simulation tools
• Positions in government laboratories, national research centres and consulting firms

For students aiming at further study, the degree also provides preparation for doctoral programmes in applied mathematics, computational science and related fields.

Why study at Syracuse University

Syracuse University offers an interdisciplinary environment where applied mathematics connects closely with engineering, computer science, physics and data science disciplines. Students benefit from faculty engaged in applied analysis, numerical computation and data-driven modelling, and have opportunities to collaborate on research projects with other departments and university research centres.

The campus provides access to modern computing resources, research seminars and applied workshops, while career services and an active local industry network help students secure internships and employment. Smaller cohort sizes mean personalised attention from faculty and supervisors, making the programme suitable for students who seek both depth in theory and practical experience applying mathematics to real-world challenges.