Indiana University Bloomington

What you'll study

The programme centres on theory and practice in numerical analysis, scientific computing and algorithm development. Core topics typically include numerical linear algebra, numerical solutions of ordinary and partial differential equations, optimisation and approximation theory, and error analysis. Students also study complementary subjects such as probability and statistics for computation, computational methods for data analysis, and high-performance computing.

• Numerical Analysis and Numerical Linear Algebra
• Scientific Computing and Algorithm Design
• Numerical Methods for PDEs and ODEs
• Optimisation and Variational Methods
• Stochastic Modelling and Computational Statistics
• High-performance computing, parallel algorithms and software engineering for simulation
• Electives in applied areas: computational biology, computational finance, image processing and machine learning

Programme structure typically combines a set of required graduate courses with electives. Students may choose a thesis (research) option or a non-thesis (coursework/project) route; the thesis route emphasises a supervised research project often carried out in collaboration with faculty in mathematics, computer science or engineering. Practical experience is encouraged through applied projects, programming assignments in languages such as Python, MATLAB and C/C++, and access to departmental and university high-performance computing resources.

Entry requirements

Applicants should hold a recognised bachelor’s degree in mathematics, applied mathematics, statistics, computer science, engineering or a closely related field with substantial mathematical content. Strong preparation in calculus, linear algebra, ordinary differential equations and basic real analysis is expected. Prior exposure to numerical methods and programming is highly desirable.

• Academic transcript demonstrating a solid undergraduate record in mathematics or a related discipline.
• Letters of recommendation from faculty or employers who can comment on quantitative ability and research or project potential.
• A personal statement outlining academic background, research or applied interests and professional goals.
• Some applicants may be asked to provide examples of prior work or to discuss preparation in advanced mathematics and programming; international applicants must meet English language proficiency requirements.

Career prospects

Graduates with a master’s in Computational Mathematics are prepared for roles that require strong quantitative and algorithmic skills. Typical positions include quantitative analyst, numerical modeller, algorithm developer, data scientist, scientific programmer and software engineer for simulation and analytics. Employers span finance, engineering consultancies, technology companies, research and development labs, government agencies and national laboratories.

The programme also provides solid preparation for further study at the PhD level in mathematics, applied mathematics, computer science or engineering for students who want to pursue research careers or academic posts.

Why study at Indiana University Bloomington

Indiana University Bloomington offers a mathematics department with a balance of pure and applied expertise, and strong connections to computational research across campus. Students benefit from collaborative opportunities with departments such as Computer Science, Informatics and Engineering, and from university-wide computing infrastructure and support for high-performance research computing.

The department’s faculty are active in areas relevant to computational mathematics, providing mentorship for both coursework and research projects. Bloomington’s research centres and institutes provide avenues for interdisciplinary projects and internships, allowing students to apply computational mathematics to real-world problems while gaining professional experience.

Overall, the programme emphasises a blend of rigorous mathematical training and practical computational skills, making it suitable for students aiming for technically demanding roles in industry or for continued research training.