University of Massachusetts Amherst

What you'll study

The programme combines advanced core mathematics with applied and computational topics to build a toolbox for modelling, analysis and numerical simulation. Full-time students typically complete a mix of required core courses, electives in applied areas and a culminating component such as a research thesis, project or comprehensive examination.

• Core topics: advanced ordinary differential equations, partial differential equations, numerical analysis, and real/functional analysis as they apply to modelling and computation.
• Applied and computational topics: mathematical modelling, scientific computing, numerical linear algebra, computational PDEs, dynamical systems, optimisation, stochastic processes and probability for applications.
• Cross-disciplinary electives: students can take electives in statistics, computer science (algorithms, machine learning), engineering disciplines or physics to tailor the degree to interests such as data science, computational engineering or mathematical biology.
• Project or thesis: options typically include a supervised research thesis, an applied capstone project with a computational component, or a comprehensive assessment, allowing students to demonstrate independent problem-solving and technical communication.
• Skills developed: mathematical modelling, numerical methods, programming for scientific computing (Python, MATLAB, or similar), data analysis, and technical writing and presentation.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, physics, engineering or a closely related quantitative field. A strong background in calculus, linear algebra, differential equations and basic real analysis is normally required. Prior coursework or experience in numerical methods and programming is highly desirable.

• Official transcripts from all post-secondary institutions attended.
• Letters of recommendation (usually two or three) that address mathematical maturity and potential for graduate study.
• A statement of purpose outlining academic preparation, research or career goals, and reasons for choosing the programme.
• Proof of English language proficiency for international applicants (accepted tests and minimums as set by the university).

Standardised test requirements (such as the GRE) and specific grade expectations vary; applicants should consult the department’s admissions page for current guidance. Relevant professional experience or research can strengthen an application if academic records are non-standard.

Career prospects

Graduates of the programme move into a broad range of technical and research roles across industry, government and academia. The training is particularly valued where advanced quantitative modelling and computational skills are required.

• Data scientist or quantitative analyst in finance, technology and consulting firms.
• Computational scientist or numerical modeller in engineering, energy, aerospace and materials sectors.
• Research or development roles in biotechnology, environmental modelling and computational biology.
• Software development and algorithm engineering positions that require strong mathematical foundations.
• Progression to PhD study in applied mathematics, computational science, engineering or related fields for students pursuing academic or research careers.

Why study at University of Massachusetts Amherst

The University of Massachusetts Amherst offers access to a well-established mathematics department with faculty working across applied analysis, numerical computation and interdisciplinary applications. Students benefit from collaborative connections with nearby engineering, computer science and statistics groups, as well as regional research centres and industry partners.

• Interdisciplinary opportunities that allow students to work on real modelling problems with faculty in engineering, physics, statistics and computer science.
• Access to institutional computing resources and labs for high-performance and scientific computing projects.
• A supportive graduate community with seminar series, research groups and opportunities to assist in teaching or research.
• Location and networks in the Pioneer Valley that facilitate internships, collaborations and connections to regional employers.