Georgetown University

What you'll study

The programme emphasises computational modelling, numerical analysis and practical software skills needed to solve complex scientific and engineering problems. Core topics typically include numerical methods for ordinary and partial differential equations, scientific computing, high‑performance and parallel computing, numerical linear algebra, and uncertainty quantification.

Students also study complementary subjects such as statistical methods for computational science, machine learning for scientific data, scientific visualisation, and data management for large simulations. The curriculum usually allows elective choices or domain‑focused modules in areas like computational biology, computational materials, fluid dynamics, climate modelling or computational finance.

A central element of the degree is a substantial applied capstone, practicum or research project in which students implement and validate a computational model, often working with faculty or an external partner. Coursework emphasizes both theoretical foundations and hands‑on programming in languages and tools commonly used in the field (for example Python, C/C++, MPI/openMP, and scientific libraries), together with experience using institutional high‑performance computing resources.

Entry requirements

Applicants are expected to hold an undergraduate degree in a quantitative discipline such as mathematics, physics, engineering, computer science, or a related field. Successful candidates typically demonstrate strong preparation in calculus, linear algebra, differential equations, and at least one programming language. Prior coursework in numerical analysis, probability/statistics or computational methods is advantageous.

Admission decisions are based on a combination of academic transcripts, a personal statement outlining research or career goals, letters of recommendation, and a curriculum vitae or résumé. Some applicants may be asked to provide examples of prior project work or coding samples. International applicants must meet Georgetown's English language proficiency requirements.

Career prospects

Graduates of computational science programmes find roles across industry, government and academia. Typical job titles include computational scientist, data scientist, quantitative analyst, simulation engineer, software engineer for scientific applications, and high‑performance computing specialist.

Career sectors include technology companies, pharmaceuticals and biotechnology, energy and environmental modelling, finance and risk analytics, aerospace and defence, national laboratories and research institutes, and policy organisations that need quantitative modelling expertise. Many graduates also continue to doctoral study in computational science, applied mathematics or related disciplines.

Why study at Georgetown University

Georgetown offers an interdisciplinary academic environment with access to faculty who work at the intersection of computation, applied mathematics and domain sciences. Located in Washington, D.C., the university provides proximity to research labs, government agencies and think tanks, enabling collaborative projects and internship opportunities that connect computational training with real‑world problems.

Students benefit from access to institutional computing resources, research centres and a network of alumni in both public and private sectors. The programme’s combination of rigorous numerical training, practical software development and applied project work prepares graduates to tackle computational challenges in a range of professional and research contexts.