Indiana University Bloomington

What you'll study

This programme combines applied mathematics, algorithm design and practical computing to build skills in numerical simulation, data analysis and high-performance computation. Core themes include numerical methods for differential equations, scientific computing, parallel and distributed computing, data assimilation and uncertainty quantification, and machine learning for scientific applications.

Typical modules and topics you can expect:

• Numerical Analysis and Scientific Computing — finite difference/element methods, stability, error analysis and solvers for large linear systems.
• High-Performance and Parallel Computing — parallel algorithms, MPI/OpenMP programming, GPU acceleration and performance tuning.
• Modelling and Simulation — mathematical modelling of physical systems, simulation workflows, multi-scale methods.
• Data Analysis for Computational Science — statistical methods, data assimilation, uncertainty quantification and reproducible workflows.
• Machine Learning for Scientific Problems — supervised and unsupervised methods applied to physical and biological datasets, surrogate modelling.
• Computational Project or Thesis — a capstone project or thesis conducted individually or with a research group, addressing an applied problem in collaboration with faculty or external partners.

The programme is interdisciplinary: students typically take courses from informatics/computer science, applied mathematics and domain departments (e.g. physics, bioinformatics, engineering). Full-time students usually complete the degree in roughly one to two years, depending on course load and whether they choose the project or thesis pathway.

Entry requirements

Applicants should hold a bachelor’s degree in a relevant quantitative discipline such as computer science, mathematics, physics, engineering or a closely related field. Successful candidates typically have demonstrated competence in calculus, linear algebra, probability/statistics and programming.

Application materials commonly requested include:

• A transcript showing a competitive undergraduate record.
• A statement of purpose outlining relevant background, research or project experience and goals.
• Letters of recommendation (usually two or three) from academic or professional referees.
• A CV or résumé detailing technical skills and project experience.
• Proof of English language proficiency for applicants whose first language is not English.

Some applicants may be asked to provide examples of programming or computational work. Prior research or internship experience is advantageous but not always required. Specific admissions criteria and any standardised testing policies are set by the university and may vary; consult the programme’s admissions page for current details.

Career prospects

Graduates move into roles that require strong quantitative, modelling and computational skills. Typical career paths include:

• Computational scientist or research programmer in academia, national laboratories or industry R&D teams.
• Data scientist / machine learning engineer working with large experimental or simulation-derived datasets.
• High-performance computing engineer or systems specialist optimising scientific codes and workflows.
• Quantitative analyst in finance or consultancy applying numerical methods to concrete problems.
• Software engineer for companies that develop simulation tools, engineering software or scientific instrumentation.

The degree also provides a pathway to doctoral study for students interested in research careers.

Why study at Indiana University Bloomington

Indiana University Bloomington offers an interdisciplinary environment that brings together faculty and resources from computing, mathematics and domain sciences. Students benefit from research centres and institutes on campus that support collaborative computational research and applied projects.

Facilities include access to campus high-performance computing infrastructure and well-established computational research groups, allowing students to gain practical experience running and optimising codes on real research systems. The Luddy School and associated departments provide a mix of theoretical and applied coursework and active faculty research areas, enabling students to tailor their studies to problems in physics, biology, engineering and beyond.

Finally, Bloomington’s research culture and links across the university create opportunities for internships, cross-disciplinary mentorship and engagement with external partners in industry and government, helping graduates move into technical and research-focused roles.