CUNY(The City University of New York)

What you'll study

The programme combines advanced theoretical mathematics with practical and computational techniques. Early coursework typically covers foundational topics such as real analysis, functional analysis, and numerical linear algebra, alongside core computational subjects including numerical analysis, scientific computing, numerical methods for partial differential equations, optimisation and computational statistics. Students also take specialised modules in areas such as spectral methods, finite element methods, uncertainty quantification, scientific machine learning, parallel and high-performance computing, and scientific visualization.

Training emphasises both rigorous analysis of algorithms (convergence, stability and error estimates) and implementation on contemporary architectures. The PhD sequence normally includes coursework, qualifying or comprehensive examinations, research seminars, teaching practicum, and an original dissertation. Students are expected to participate in department and city-wide seminars, present research at conferences, and may engage in interdisciplinary collaborations with applied sciences, engineering, computer science, or data science groups across CUNY and local institutions.

Entry requirements

Applicants should hold a strong undergraduate or master’s degree in mathematics, applied mathematics, computational science, engineering, physics or a closely related discipline. A solid background in advanced calculus, linear algebra, differential equations, real analysis and basic numerical methods is expected. Programming experience in languages commonly used in scientific computing (for example Python, C/C++, MATLAB or Fortran) and familiarity with numerical libraries is strongly recommended.

Typical application materials include official transcripts, a curriculum vitae, a statement of academic and research interests, and at least three academic references able to comment on mathematical and research potential. A master’s degree with a research component is advantageous but not strictly required for exceptional applicants with outstanding preparation. International applicants must demonstrate English proficiency according to university policy.

Career prospects

Graduates with a PhD focused on computational mathematics go on to careers in academic research and teaching, national and private research laboratories, and industry roles that require advanced modelling and algorithm development. Typical roles include university faculty, research scientist, computational scientist, numerical analyst, quantitative developer in finance, data scientist with a focus on model-driven methods, and software engineer for high-performance and scientific computing applications.

Because the programme develops both theoretical and practical skills, alumni are also well placed for positions in engineering firms, energy and climate modelling groups, biotechnology, defence research, and technology companies building infrastructure for large-scale computation and machine learning.

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

CUNY’s Graduate Center and its constituent colleges provide a collaborative environment in the heart of New York City, with access to a broad network of faculty across mathematics, computer science, engineering and the applied sciences. Students benefit from regular seminars, workshops and interdisciplinary research opportunities with nearby universities, national labs and industry partners.

The department offers supervision from faculty active in numerical analysis, scientific computing, optimisation and data-driven modelling, and provides access to computing resources and HPC facilities through university and city-wide arrangements. CUNY’s emphasis on diversity, mentorship and public engagement supports a wide range of students, and teaching opportunities across the university help develop communication and pedagogy skills valued in academic careers. The programme’s location in a major research and commercial hub also facilitates internships, collaborations and career networking.