John Hopkins University

What you'll study

The programme emphasises core mathematical theory alongside practical and computational methods. Students take a mixture of required and elective courses that develop skills in analysis, numerical computation, and algorithm design, and may undertake a research project or thesis.

• Core topics: Real and complex analysis, functional analysis, partial differential equations, and probability and stochastic processes as a theoretical foundation for computational work.
• Computational and numerical methods: Numerical linear algebra, numerical solution of ODEs and PDEs, spectral methods, finite element methods, and numerical optimisation.
• Scientific computing and algorithms: High-performance computing, parallel algorithms, computational complexity, and algorithmic aspects of large-scale data processing.
• Applied modelling: Computational statistics, data assimilation, inverse problems, machine learning methods for scientific data, and mathematical modelling in physics, biology or finance.
• Project or thesis: A substantial independent project or thesis supervised by a faculty member, often collaborating with research groups or centres across the university (for example in applied mathematics, engineering, or data science).

Typical structure

Students generally complete a combination of coursework and a research component. The taught portion covers foundational and advanced modules, while the capstone may be a supervised research thesis or an applied project. Flexible elective options allow students to tailor the degree to computational science, data-driven research, or industry-oriented applications.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering or a closely related quantitative discipline with strong mathematical content. Typical preparation includes undergraduate courses in calculus, linear algebra, differential equations, real analysis and some programming experience.

• Academic record: A solid undergraduate transcript showing strong performance in mathematics and quantitative courses.
• Mathematical background: Demonstrable competence in rigorous analysis and linear algebra; experience with numerical methods is advantageous.
• Programming skills: Familiarity with at least one programming language commonly used in scientific computing (such as Python, C/C++, or MATLAB) is expected.
• Supporting documents: Transcripts, letters of recommendation from academic or professional referees, and a statement of purpose describing research interests and career goals.
• English language proficiency: Where applicable, evidence of English proficiency is required according to university policy.

Career prospects

Graduates are prepared for careers that require strong mathematical modelling and computational skills. Common destinations include roles in quantitative finance, data science and analytics, scientific and engineering research, software development for scientific computing, and government or national laboratories.

• Quantitative analyst or quantitative developer in finance and trading firms.
• Data scientist or machine learning engineer in technology and biotech companies.
• Research scientist or computational modeller in academia, industry research labs or government agencies.
• Software engineer focusing on high-performance computing, numerical libraries or simulation tools.
• Further study at the doctoral level in mathematics, applied mathematics, computational science or related fields.

Why study at Johns Hopkins University

Johns Hopkins offers an interdisciplinary environment with strong ties between mathematics, engineering, medical research and data science, providing students with opportunities to work on real-world computational problems. Faculty in applied and computational mathematics conduct active research in numerical analysis, scientific computing and data-driven modelling, and students can engage with research centres and cross-department collaborations.

Access to state-of-the-art computing resources, collaboration with research groups across the institution, and a location within a research-intensive university make the programme particularly suitable for students aiming to pursue research or technical careers that demand advanced computational mathematics expertise.