Indiana University Bloomington

What you'll study

The programme emphasises the formulation, analysis and numerical solution of problems arising in the physical, biological and social sciences. Core topics typically include ordinary and partial differential equations, numerical analysis and scientific computing, optimisation, stochastic processes and probability, and advanced linear algebra. Elective and specialised modules often cover subjects such as mathematical modelling, computational methods for PDEs, high-performance computing, inverse problems, dynamical systems, mathematical biology, data analysis and machine learning.

Students can expect a mix of theory, applied coursework and computational projects: seminars and reading courses expose students to current research, while practical laboratory-style classes and team projects focus on implementing algorithms and validating models on applied problems. Many programmes offer both thesis and non-thesis (project or coursework) options, allowing students to pursue a supervised research project or a sequence of advanced coursework depending on career aims.

Entry requirements

Successful applicants normally hold a bachelor’s degree in mathematics, applied mathematics, statistics, physics, engineering, computer science or a closely related quantitative discipline. Typical preparation includes single- and multivariable calculus, linear algebra, differential equations, probability or statistics, and some programming experience (for example Python, MATLAB, C/C++ or R).

Admission requires a transcript of academic records, a statement of purpose outlining research interests and objectives, and academic references. Graduate test requirements and other documentation for international applicants (such as proof of English language proficiency) follow university policy; prospective students are advised to consult the department for current guidance.

Career prospects

Graduates of applied mathematics programmes move into a wide range of roles in industry, government and academia. Common career paths include data scientist or analyst, quantitative analyst in finance, computational scientist or modeller in engineering and physical sciences, software engineer for scientific applications, and roles in optimisation and operations research. The strong computational and modelling training also prepares graduates for doctoral study in applied mathematics, computational science, engineering and related disciplines.

Graduates find opportunities in sectors such as technology, finance, healthcare and pharmaceuticals, energy, defence and national labs, as well as research centres and academic institutions. Indiana University's career services and departmental networks help students connect with internships, collaborative research projects and regional employers.

Why study at Indiana University Bloomington

Indiana University Bloomington combines a strong mathematics faculty with interdisciplinary collaborations across campus. Students benefit from links with the Luddy School of Informatics, Computing, and Engineering and access to campus research computing resources and centres that support computational science. The department hosts seminars and research groups in applied and computational mathematics, providing opportunities for students to engage with active research in modelling, numerical analysis and scientific computing.

The Bloomington campus offers a supportive graduate environment, with advising, teaching and research assistantship opportunities and access to professional-development services. For students seeking hands-on computational experience and connections with regional research organisations, the programme provides a balance of theoretical depth and applied, project-based training suited to both industry and academic careers.