Massachusetts Institute of Technology

What you'll study

The curriculum integrates foundational courses in mathematics, computer science and biology with specialised modules in computational biology and bioinformatics. Students take core subjects such as multivariable calculus, linear algebra, probability and differential equations alongside introductory and intermediate programming, data structures, and algorithms. In biology, required topics include cellular and molecular biology, genetics, biochemistry and systems biology.

Program‑specific coursework focuses on computational and quantitative methods applied to biological data: statistical genomics and population genetics, machine learning for biological data, sequence analysis and alignment algorithms, structural bioinformatics, network and dynamical systems approaches to cell signalling, and numerical methods for PDE/ODE models in biology. Laboratory and wet‑lab literacy are emphasised through experimental design and hands‑on lab classes so computational analyses are grounded in real experimental constraints.

Students are expected to participate in mentored research (for example through MIT’s undergraduate research programmes), and to complete an advanced project or capstone that applies computational methods to an open biological problem. Electives permit deeper study in areas such as neuroinformatics, imaging and computational pathology, synthetic biology, biophysics, or advanced machine learning.

• Mathematics core: calculus sequence, linear algebra, probability & statistics, differential equations
• Computer science core: programming, data structures, algorithms, machine learning, scientific computing
• Biology core: molecular & cellular biology, genetics, biochemistry, systems biology
• Computational biology specialisms: bioinformatics algorithms, statistical genomics, structural bioinformatics, network biology, computational systems biology
• Research and capstone: mentored laboratory/computational research, thesis or project presentation

Entry requirements

Admission to MIT is highly selective and evaluated holistically. Successful applicants typically present an exceptional academic record with strong preparation in mathematics (including experience with calculus), science (biology and chemistry), and some programming experience. Advanced coursework such as AP, IB higher‑level, A‑levels, or equivalent university courses in calculus, linear algebra, physics and laboratory biology are advantageous.

Typical application components include school transcripts, teacher recommendations (often from math and science teachers), personal essays, and records of extracurricular engagement—particularly research experience, computational projects, or independent laboratory work. Evidence of aptitude for quantitative problem solving and programming (coding projects, competitions, coursework) strengthens an application. English language proficiency evidence may be required for applicants whose first language is not English, according to institutional policy.

Career prospects

Graduates are well prepared for roles that require quantitative analysis of biological data. Common career paths include computational biology and bioinformatics positions in biotech and pharmaceutical companies, genomic diagnostics and precision medicine firms, and agricultural or environmental genomics organisations. Graduates also work as data scientists, machine learning engineers, and software developers focused on life‑science applications.

The degree is excellent preparation for further study: many students progress to PhD programmes in computational biology, bioinformatics, biostatistics, or related fields, or to professional programmes in medicine and quantitative biosciences. Research experience during the undergraduate programme can directly lead to research scientist roles and academic postgraduateship opportunities.

Why study at Massachusetts Institute of Technology

MIT offers a strong interdisciplinary environment where mathematics, computer science and biology are closely integrated. Students benefit from world‑class faculty working at the forefront of computational genomics, systems biology and machine learning for biology, as well as access to cutting‑edge facilities and a large, active undergraduate research ecosystem.

Opportunities for collaboration across departments and institutes, including hands‑on research with faculty labs and industry partners, provide real‑world experience solving contemporary biological problems. The culture emphasises innovation, entrepreneurship and translating computational discoveries into practical tools and technologies—valuable for students aiming for either industry leadership or academic careers.