Chapman University

What you'll study

The programme emphasises mathematical modelling, numerical analysis and software engineering for large-scale scientific problems. Core topics typically include numerical methods for differential equations, scientific computing, high-performance computing (HPC) and parallel programming, computational linear algebra, and uncertainty quantification. Courses also cover applied areas such as computational fluid dynamics, computational biology, optimisation, and machine learning for scientific data.

Students follow a combination of core modules, electives and a substantial culminating experience — either a project-based practicum, industry internship or a supervised research thesis. Typical module examples include:

• Numerical Methods and Scientific Computing
• High-Performance and Parallel Computing
• Computational Modelling of Physical Systems
• Statistical Methods and Data Analysis for Science
• Advanced Topics in Machine Learning for Science
• Scientific Visualisation and Data Management

Teaching mixes lectures, coding laboratories and project work. The programme places strong emphasis on practical experience with languages and tools common in the field (for example Python, C/C++, MPI/OpenMP, and specialised libraries for numerical linear algebra and simulation), and on reproducible computational research practices.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, computer science, physics, engineering or a closely related discipline. Competence in calculus, linear algebra and basic programming is required; some preparation in differential equations and numerical methods is desirable. Admissions typically consider a combination of academic transcripts, a statement of purpose outlining research or career goals, a CV or résumé, and one or more academic or professional references.

Chapman may request supplementary materials for applicants from non-technical backgrounds to demonstrate quantitative readiness. International applicants must demonstrate English proficiency in line with the university’s standard requirements. The programme may have options for part-time or full-time study; prospective students should consult the department for available modes.

Career prospects

Graduates of the Master’s in Computational Science go on to roles where advanced numerical and data-driven skills are required. Typical job titles include computational scientist, simulation engineer, data scientist for scientific applications, quantitative analyst, research software engineer, and HPC engineer. Alumni find employment in sectors such as aerospace and defence, energy and utilities, biotechnology and pharmaceuticals, engineering consultancies, environmental modelling, finance, and government research laboratories.

The programme’s project and practicum components help students build a portfolio of applied work for employers. Many graduates also use the degree as preparation for doctoral study in computational science, applied mathematics, or related disciplines.

Why study at Chapman University

Chapman offers a small-class, applied-learning environment with close faculty supervision and opportunities for interdisciplinary collaboration across science, engineering and business units. The university provides access to campus computing resources and specialised laboratories, and emphasises experiential learning through practicum projects and local industry partnerships. Located in Southern California, Chapman affords proximity to a large regional cluster of technology, biotech and engineering employers, supporting internships and collaboration.

Students benefit from personalised career services, active research groups in numerical methods and data science, and a curriculum designed to combine theoretical foundations with practical software skills — preparing graduates to address real-world computational challenges.