John Hopkins University

What you'll study

The programme combines core coursework in analysis and computation with elective study and a research or project component. Typical core topics include partial differential equations, numerical analysis, scientific computing, linear and nonlinear optimisation, and probability and stochastic processes. Students also study mathematical modelling across applications such as fluid dynamics, mathematical biology, imaging and inverse problems, and data-driven methods.

Structure is flexible: students follow a set of required graduate-level courses, choose advanced electives to deepen in areas such as computational PDEs, numerical linear algebra, dynamical systems, uncertainty quantification and machine learning for applied problems, and complete either a supervised research thesis or a substantial applied project. Regular seminars, journal clubs and departmental colloquia are integral, and many students engage in collaborative projects with other departments and affiliated research centres.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related quantitative discipline. Strong preparation in multivariable calculus, linear algebra, differential equations, basic real analysis, and undergraduate-level probability or statistics is expected. Demonstrated competence in programming or numerical computing (for example MATLAB, Python or C++) is advantageous.

Typical application materials include academic transcripts, a statement of purpose outlining research interests and goals, letters of recommendation from academic or professional referees, and a CV. International applicants must demonstrate proficiency in English through an accepted test unless exempt; other standardised tests may be optional or considered at the programme’s discretion.

Career prospects

Graduates go on to a mix of careers in industry, government and academia. Common roles include data scientist, quantitative analyst, computational scientist, modelling engineer, and software developer for scientific applications. Alumni also pursue doctoral study in applied mathematics and related fields. Employment sectors include finance, technology, biotech and pharmaceutical companies, national laboratories and government research agencies, consulting firms, and academic and medical research groups.

Why study at John Hopkins University

Johns Hopkins offers a strong research environment with active faculty in computational mathematics, mathematical biology, imaging and data science, and close connections to engineering, medicine and public health. Students benefit from interdisciplinary collaborations across campus and with affiliated research institutes, access to high-performance computing resources, and a range of seminars and workshops that expose them to current problems and methods. The university’s location and professional networks provide opportunities for internships and industry engagement, while career services and alumni connections support transitions into research or professional roles.