Dartmouth College

What you'll study

This programme emphasises the mathematical foundations and computational techniques used to model, analyse and solve large‑scale scientific and engineering problems. Core topics typically include numerical analysis, numerical linear algebra, scientific computing, and the numerical solution of ordinary and partial differential equations. Students also study related areas such as optimisation, probability and statistics for computation, and algorithmic aspects of numerical methods.

• Numerical Analysis and Error Theory
• Numerical Linear Algebra and Matrix Computations
• Scientific Computing and High‑Performance Computing (HPC)
• Computational Partial Differential Equations
• Numerical Optimisation and Inverse Problems
• Computational Statistics and Data‑driven Methods
• Advanced topics/electives: machine learning, scientific visualisation, stochastic modelling
• Capstone project or master's thesis involving original computational work and software development

The programme typically combines rigorous coursework with a substantial supervised research or capstone project. Projects emphasise reproducible computational experiments, use of numerical libraries, parallel computing and software engineering best practice.

Entry requirements

Successful applicants normally hold a strong undergraduate degree in mathematics, applied mathematics, computer science, engineering, physics or a closely related quantitative discipline. Typical background preparation includes multivariable calculus, linear algebra, differential equations, basic real analysis and proficiency in at least one programming language (for example Python, C/C++ or MATLAB).

• Application materials generally expected: academic transcripts, a personal statement describing research and computational interests, a curriculum vitae and two or three letters of recommendation.
• Prior coursework or research experience in numerical methods, scientific computing, or applied mathematics is advantageous.
• International applicants usually need to demonstrate English language proficiency where applicable.

Career prospects

Graduates with a master’s in Computational Mathematics move into roles that require strong quantitative and computational skills across industry, government and academia. Common career paths include:

• Data scientist and machine learning engineer – applying statistical and numerical methods to large data sets.
• Quantitative analyst or model developer in finance – building and implementing numerical models for pricing, risk and strategy.
• Computational scientist or research engineer – developing simulation codes for physics, engineering, climate science or bioinformatics.
• Software engineer for scientific and engineering applications – focusing on performance, parallelism and numerical reliability.
• Further academic study – progression to PhD programmes in applied mathematics, computational science or related fields.

The programme’s emphasis on project work and software development prepares graduates to contribute immediately to teams that require reproducible computational research and production‑quality numerical code.

Why study at Dartmouth College

Dartmouth combines a strong tradition in mathematics with close interdisciplinary links across engineering, computer science and the natural sciences. Students benefit from small class sizes and direct access to faculty mentors, enabling tailored supervision for computational projects and theses.

• Interdisciplinary collaboration: opportunities to work with researchers in the Thayer School of Engineering, computer science and domain science groups on applied computational problems.
• Research and computing resources: access to institutional computing facilities and support for parallel and high‑performance computing workflows.
• Hands‑on project emphasis: a programme structure that stresses applied projects, reproducibility and software engineering practices valued by employers.
• Close alumni and industry networks: connections that support internships, industry projects and career transitions into tech, finance, government labs and research institutes.

Overall, the programme is well suited to students seeking rigorous mathematical training paired with practical computational experience and pathways into both industry and further research.