Chapman University

What you'll study

The PhD in Computational Science combines advanced coursework, original research and dissertation work. Early years emphasise core methods such as numerical analysis, scientific computing, high-performance computing, applied mathematics, statistical modelling and machine learning, together with domain-specific applications in areas like computational physics, computational biology, data science and engineering.

• Core modules and topics: numerical methods for PDEs and ODEs, linear algebra for large systems, computational statistics, optimisation, uncertainty quantification and parallel algorithms.
• Advanced and elective topics: machine learning and deep learning for scientific data, data visualisation, stochastic modelling, computational fluid dynamics, biomolecular simulation and inverse problems.
• Research training: seminars, journal clubs, scientific writing and research ethics. Students gain hands-on experience with high-performance computing, GPU programming and software engineering practices needed for reproducible computational research.
• Structure: the programme normally combines 1–2 years of advanced coursework and qualifying assessments followed by concentrated research leading to a dissertation under the supervision of a faculty advisor. Students may also undertake teaching or project supervision duties as part of professional development.

Entry requirements

Applicants are expected to hold a relevant master's degree or a strong bachelor’s degree with substantial postgraduate or professional experience in a quantitative field such as computer science, applied mathematics, physics, engineering, statistics or a related discipline. Key elements of a competitive application include:

• Academic background: demonstrated coursework or experience in calculus, linear algebra, probability and statistics, and programming (Python, C/C++, or similar).
• Research potential: evidence of research ability, such as a master’s thesis, publications, strong letters of recommendation, or relevant industry research experience.
• Supporting materials: a research statement describing proposed areas of interest, CV, academic transcripts and letters of recommendation. Applicants whose first language is not English must meet the university's English proficiency requirements.
• Other considerations: fit with faculty research interests and availability of a willing supervisor are important; applicants should identify potential advisers and relevant research groups when applying.

Career prospects

Graduates of a PhD in Computational Science pursue a wide range of roles across academia, industry and the public sector. Typical career destinations include:

• Faculty and postdoctoral positions in universities and research institutes, leading independent research programmes and teaching.
• Research scientist or senior data scientist roles in technology companies, national laboratories and industrial R&D groups, focusing on modelling, simulation and algorithm development.
• Positions in finance, energy, aerospace, biotechnology and healthcare applying computational modelling, risk analysis and data-driven decision-making.
• Roles in high-performance computing centres, software engineering teams building scientific codes, and science policy or consulting where quantitative expertise is required.

Why study at Chapman University

Chapman University offers a collaborative, interdisciplinary environment within its Schmid College of Science and Technology and related departments, enabling students to work across chemistry, physics, biology, engineering and computer science. Small cohort sizes provide close mentorship from faculty and opportunities for tailored research training. Students have access to campus computational resources and receive hands-on experience with modern tools for high-performance and data-intensive computing.

The university's location in Southern California also supports connections with regional industry, national laboratories and start-ups, facilitating internships, collaborations and career networking. Chapman emphasises professional development, teaching experience and preparation for both academic and non-academic career paths for doctoral students in computational science.