CUNY(The City University of New York)

What you'll study

The programme combines rigorous mathematical theory with practical computational techniques. Core topics typically include numerical analysis, numerical linear algebra, and scientific computing, together with coursework in partial differential equations, optimisation and approximation theory. Students learn to design, analyse and implement algorithms for solving large‑scale problems that arise in physics, engineering, finance and data science.

• Numerical Analysis: error analysis, convergence, stability of numerical methods for ODEs and PDEs.
• Numerical Linear Algebra: iterative methods, direct solvers, preconditioning and eigenvalue algorithms.
• Scientific Computing and High‑Performance Computing: parallel computing, code optimisation, use of libraries (e.g. BLAS, LAPACK) and modern programming environments.
• Computational PDEs and Modelling: finite difference, finite element and spectral methods for elliptic, parabolic and hyperbolic problems.
• Optimisation and Inverse Problems: convex and non‑convex optimisation, variational methods and regularisation techniques.
• Stochastic Methods and Numerical Probability: Monte Carlo methods, stochastic differential equations and uncertainty quantification.
• Electives: machine learning for scientists, computational biology, numerical methods in finance, scientific visualization and advanced algorithm design.
• Research Project or Thesis: a substantial computational project or supervised thesis that typically involves implementing algorithms, running simulations and reporting results in written and oral form.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, physics, engineering, computer science or a closely related discipline, with demonstrated competence in calculus, linear algebra and basic real analysis. Practical programming experience (for example in Python, MATLAB, C/C++ or Fortran) and familiarity with differential equations are strongly recommended.

• Official academic transcripts from all post‑secondary institutions attended.
• A personal statement outlining academic interests, computational experience and career goals.
• Letters of recommendation, typically two or three, attesting to academic preparation and potential for graduate study.
• Some programmes may request a sample of previous academic work or an interview; standardised tests and exact requirements vary by campus and are considered on a case‑by‑case basis.
• Applicants whose first language is not English will need to meet the university’s English proficiency requirements as set by the admitting campus.

Career prospects

Graduates develop quantitative, algorithmic and software skills that are in demand across a wide range of sectors. Career paths commonly pursued by alumni include quantitative analyst roles in finance, data scientist or machine learning engineer positions, computational scientist or research engineer posts in industry and national labs, and software development roles focused on scientific applications.

• Data science and analytics in technology and finance.
• Quantitative modelling and risk analysis in banking and insurance.
• Scientific and engineering simulation in aerospace, energy and manufacturing.
• Research and development roles in healthcare, computational biology and environmental modelling.
• Further academic study (PhD) in applied mathematics, computational science or related fields.

Why study at CUNY(The City University of New York)

CUNY offers access to a broad network of campuses, research centres and faculty whose work spans pure and applied mathematics, computation and data science. Studying at CUNY provides opportunities to collaborate with researchers across engineering, physics and computer science, and to pursue internships and partnerships with New York City’s dense ecosystem of finance, tech and research organisations.

• Faculty with active research in numerical analysis, scientific computing, optimisation and related areas.
• Proximity to industry and national laboratories for internships, collaborative projects and applied research placements.
• Diverse student body and support services, including computing facilities and access to high‑performance resources on some campuses.
• Flexible programme pathways allowing a mix of coursework and research, suitable for both professional advancement and preparation for doctoral study.