Rochester Institute of Technology

What you'll study

The programme combines rigorous mathematical foundations with applied and computational techniques. Typical core topics include advanced linear algebra, real and complex analysis, ordinary and partial differential equations, numerical analysis, scientific computing and mathematical modelling. You will also study applied subjects such as optimisation, probability and statistics for modelling, computational methods for data, and specialised electives drawn from areas like dynamical systems, numerical linear algebra, stochastic processes, and mathematical methods in imaging and signal processing.

Students normally choose between a thesis (research) option and a project/coursework option. The thesis route emphasises supervised research and preparation for doctoral study, while the project route focuses on an applied capstone project that often involves collaboration with faculty in engineering, computer science or industry partners. Coursework frequently includes hands-on computing assignments, use of high-performance computing resources, and software tools for numerical simulation and data analysis.

Entry requirements

• Bachelor’s degree in mathematics or a closely related discipline (for example physics, engineering, computer science or statistics) with strong mathematical preparation.
• Solid background in calculus, linear algebra, differential equations and introductory real analysis; prior coursework in numerical methods, probability or programming is highly recommended.
• Official transcripts and a statement of purpose that describes academic and professional goals; letters of recommendation are typically required.
• Proof of English proficiency for applicants whose first language is not English, such as IELTS or TOEFL, where required by the university.
• GRE scores may be requested in some cases; check the programme’s admissions page for current testing policies. Relevant professional experience can strengthen an application for applicants from non-academic backgrounds.

Career prospects

Graduates from this programme move into a wide range of quantitative and computational roles. Typical careers include applied mathematician, data scientist, quantitative analyst, computational scientist, modelling and simulation engineer, and roles in software development where strong numerical skills are required. Employers span industry sectors such as finance, engineering, defence, healthcare, imaging and technology, as well as national labs and government agencies.

The degree also prepares students for further research, including entry to doctoral programmes in mathematics, applied mathematics, computational science and related interdisciplinary fields.

Why study at Rochester Institute of Technology

• Applied and interdisciplinary focus: RIT emphasises hands-on, application-driven training that integrates mathematics with computing, engineering and the sciences.
• Research and computing resources: Access to computational facilities and faculty engaged in applied research areas such as numerical simulation, imaging, optimisation and data analysis.
• Collaborative opportunities: Strong ties between mathematics, computer science, engineering and industry enable cross-disciplinary projects and capstones with practical impact.
• Career support: RIT’s career services, employer connections and experiential learning culture help students secure internships, projects and roles in industry or government sectors.
• Location and community: Situated in a region with a strong technology and imaging presence, the university offers a community of researchers and practitioners in applied mathematics and computational science.