Dartmouth University

What you'll study

Although Dartmouth does not confer a single consolidated "Mathematics and Computer Science" master’s degree, students pursuing an interdisciplinary graduate pathway will study core topics from both fields. Coursework typically combines theoretical and applied modules drawn from graduate offerings in Computer Science, Mathematics, and Engineering.

• Core computer science topics: algorithms and data structures at an advanced level, theory of computation, operating systems, databases, machine learning, and software engineering practices.
• Core mathematics topics: real and complex analysis, linear algebra and matrix computations, probability and stochastic processes, optimization theory, and mathematical foundations for machine learning.
• Bridging and applied modules: numerical analysis, scientific computing, cryptography, computational geometry, high-performance computing, and data science courses that emphasise mathematical modelling and statistical inference.
• Research and project work: an independent thesis or a substantial capstone project developed in collaboration with a faculty advisor from Computer Science and/or Mathematics; project topics often address applied problems in data science, computational biology, robotics, or systems design.
• Electives and seminars: advanced seminars in specialised areas such as deep learning, computational topology, algorithmic game theory, or optimization for machine learning, allowing students to tailor study to academic or industry goals.

Entry requirements

Admission to a graduate pathway combining mathematics and computer science at Dartmouth is competitive and typically requires a strong academic background in quantitative subjects. Typical entry expectations include:

• Academic qualifications: a bachelor’s degree in computer science, mathematics, engineering, physics or a closely related discipline with strong performance in relevant coursework.
• Preparation: evidence of solid foundations in programming, discrete mathematics, calculus (including multivariable calculus), linear algebra and probability/statistics. Prior exposure to algorithms and data structures is highly desirable.
• Application materials: academic transcripts, a statement of purpose outlining research or professional goals, letters of recommendation (usually two or three), and a CV highlighting relevant projects or work experience.
• Standardised tests and additional evidence: some graduate programmes or fellowship applications may accept or request GRE scores, though policies vary; international applicants should demonstrate English language proficiency where required. Applicants with significant research, industry experience or strong academic publications can strengthen a candidacy.
• Faculty alignment: successful applicants normally identify potential faculty advisors whose research interests span computer science and mathematics to support an interdisciplinary programme or project.

Career prospects

Graduates who combine advanced training in mathematics and computer science are in demand across industry, academia and the public sector. Typical career paths include:

• Data scientist / machine learning engineer: applying statistical modelling, optimisation and algorithmic techniques to large-scale data problems in technology, finance, healthcare and government.
• Quantitative analyst / quant: developing mathematical models for pricing, risk management and trading in financial services.
• Research scientist (industry or academic): conducting research in artificial intelligence, computational theory, numerical analysis or related fields, often progressing to PhD study.
• Software engineer / systems architect: building performant, mathematically-informed systems for tasks requiring careful algorithmic design and numerical reliability.
• Applied mathematician / computational scientist: working on simulation, optimisation and modelling projects in energy, aerospace, biology and engineering.

Why study at Dartmouth College

Dartmouth offers distinctive strengths for interdisciplinary graduate work at the intersection of mathematics and computer science. The campus is relatively small compared with large research universities, which fosters close faculty–student interaction and the opportunity to work directly with researchers across departments. The Thayer School of Engineering and the Computer Science Department both support collaborative projects that bridge theory and application, and students benefit from Dartmouth’s liberal arts environment, which encourages breadth alongside technical depth.

Students pursuing an interdisciplinary pathway at Dartmouth typically find strong mentorship, access to cross-disciplinary seminars and research groups, and opportunities to engage with industry through internships and alumni networks. Prospective applicants should reach out to potential advisors in Computer Science and Mathematics to design a programme of study tailored to their research and career goals.