Emory University

What you'll study

Coursework balances theoretical foundations and practical computing. Typical areas of study include:

• Real and complex analysis: advanced topics in measure theory, functional analysis and complex variables that underpin rigorous numerical methods.
• Numerical analysis and scientific computation: numerical linear algebra, error analysis, finite difference/finite element methods and algorithms for PDEs.
• Computational methods: high-performance computing concepts, numerical optimization, approximation theory and spectral methods.
• Probability and statistics for computation: stochastic modelling, Monte Carlo methods and statistical inference relevant to computational experiments.
• Applied mathematics and modelling: dynamical systems, mathematical modelling of physical and biological systems, and inverse problems.
• Programming and software tools: practical training in scientific programming (e.g. Python, MATLAB, C/C++), numerical libraries and version control.

Students normally complete a combination of core courses and electives and may undertake a capstone project or a supervised thesis project that applies computational methods to a substantive problem.

Entry requirements

Applicants should hold a bachelor's degree in mathematics, applied mathematics, computer science, engineering or a closely related discipline, with evidence of strong quantitative preparation. Typical prerequisites include coursework in linear algebra, multivariable calculus, differential equations and an introduction to mathematical analysis. Practical experience with programming or numerical computation is advantageous.

• Completed application and official transcripts from previous institutions.
• Letters of recommendation that address mathematical ability and preparedness for graduate study.
• A statement of purpose outlining academic interests, relevant experience and goals.
• Evidence of mathematical preparation through prior coursework; standardized test requirements vary, so applicants should consult the graduate admissions guidance.

Career prospects

Graduates are prepared for a range of roles in industry, government and academia where advanced quantitative and computational skills are valued. Common career paths include:

• Data scientist, machine learning engineer or quantitative analyst in finance and technology firms.
• Computational scientist or numerical modeller in engineering, energy, environmental science and bioinformatics.
• Software developer focused on scientific and high-performance computing applications.
• Research or technical roles in national labs, consultancies and government agencies.
• Further graduate study (PhD) in mathematics, applied mathematics, computational science or related fields.

Why study at Emory University

Emory offers a supportive environment with access to faculty who work across pure and applied mathematics, computer science and interdisciplinary research centres. The programme benefits from small class sizes and opportunities for close mentorship, collaborative projects and participation in departmental seminars and reading groups. Located in Atlanta, Emory provides proximity to a vibrant technology and healthcare ecosystem, which can facilitate internships and applied collaborations. Students also have access to campus computational resources and opportunities to engage with research groups across the university.