CUNY(The City University of New York)

What you'll study

This master's combines core training in mathematical foundations and algorithmic techniques with applied modules that emphasise hands-on computing. Typical subjects include numerical analysis, scientific computing, parallel and high-performance computing, computational modelling and simulation, statistical methods for data analysis, and machine learning for scientific data.

• Core topics: numerical methods for differential equations, linear algebra for large systems, optimisation, uncertainty quantification and error analysis.
• Computing and tools: parallel programming (MPI/OpenMP), GPU computing, software engineering for scientific codes, version control and workflow automation.
• Data and modelling: statistical modelling, time-series analysis, inverse problems, data assimilation and scientific visualisation.
• Electives and domain applications: bioinformatics, computational fluid dynamics, geophysical modelling, computational finance, or computational materials, allowing specialisation toward particular research or industry areas.
• Capstone or thesis: a substantial applied project or research thesis that typically involves developing algorithms or simulations and applying them to real-world datasets or experimental problems.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in a quantitative discipline such as mathematics, physics, engineering, computer science or a related science with a solid grounding in calculus and linear algebra. Demonstrable programming experience (for example in Python, C/C++ or Fortran), exposure to numerical methods and a basic course in probability or statistics are usually required or strongly recommended.

• Academic transcripts showing relevant quantitative coursework.
• A personal statement outlining your computing and research experience and goals.
• Letters of recommendation from academic or professional referees who can attest to your technical ability.
• Some campuses or programs may request GRE scores, while others treat them as optional; check the specific campus requirements. International applicants must meet English language proficiency requirements.
• Professional or research experience in computational projects can strengthen applications, and part-time or continuing‑education pathways are frequently available for working professionals.

Career prospects

Graduates gain skills sought across academia, industry and government. Typical roles include computational scientist, data scientist, scientific software engineer, high‑performance computing specialist, quantitative analyst and modelling engineer. Employers span technology companies, financial institutions, research laboratories, engineering consultancies, healthcare and pharmaceutical firms, and government agencies.

The programme also prepares students for further research at the doctoral level, or for roles that combine domain expertise with advanced computing such as computational biology, climate modelling and computational materials science.

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

CUNY offers access to the vibrant research and industry ecosystem of New York City, with opportunities for collaboration across its campuses, local research institutes and industry partners. The university’s public mission emphasises affordability and diversity, providing a range of paths for full‑time and part‑time students as well as strong links to internships and applied projects in the region.

Students benefit from faculty who conduct applied computational research, shared computing resources and connections to city‑wide initiatives in data science and engineering. The programme’s interdisciplinary structure allows you to tailor study to specific scientific domains while gaining transferable skills in high‑performance computing, numerical methods and data analysis.