Brandeis University

What you'll study

This programme blends coursework and a substantial research or practicum component to develop skills in computational analysis, mathematical modelling and biological interpretation. Core themes include statistical methods for biological data, algorithms for sequence and structural analysis, machine learning for genomics, dynamical systems and network modelling of biological processes, and practical programming for data science in biology.

• Foundational modules: probability and statistics, linear algebra and optimisation for biological applications, and introductory computational biology.
• Computational and algorithmic topics: sequence analysis, genome informatics, graph and network algorithms, and scalable data structures.
• Data science and machine learning: supervised and unsupervised learning, deep learning for biological images and sequences, and reproducible workflows in R and Python.
• Mathematical modelling: ordinary and partial differential equation models, stochastic processes in biology, and parameter inference for dynamical systems.
• Specialist electives: systems biology, structural bioinformatics, single‑cell data analysis, population genetics, and computational neuroscience, depending on faculty availability.
• Practical experience: a capstone research project or practicum carried out in collaboration with a Brandeis research lab, computational core facility or an external partner; students gain hands‑on experience with real biological datasets and high‑performance computing resources.

Entry requirements

Applicants should hold a bachelor’s degree (or equivalent) in a quantitative or life‑science discipline such as biology, computer science, mathematics, statistics, engineering or a related field. Successful candidates typically demonstrate preparation in calculus and linear algebra, introductory probability and statistics, and some programming experience (for example in Python, R or MATLAB).

• Transcript showing a strong academic record in relevant subjects.
• A statement of purpose describing academic background, research or professional interests, and reasons for applying to this programme.
• Two or three academic or professional letters of recommendation.
• Evidence of programming ability and quantitative coursework; applicants lacking specific courses may be admitted conditionally or asked to complete prerequisite modules.
• International applicants must demonstrate English language proficiency according to Brandeis University requirements.

Career prospects

Graduates are prepared for a wide range of roles at the interface of computation and biology. Typical career paths include positions as bioinformatics analysts, computational biologists, data scientists within pharmaceutical and biotechnology companies, genomics or diagnostics firms, and computational roles in healthcare analytics. The programme also provides solid preparation for continued academic research at PhD level or for roles in translational research and technology development.

• Industry: bioinformatics scientist, computational genomics analyst, data scientist in biotech or pharma.
• Clinical and diagnostic labs: pipeline development, variant interpretation and assay analytics.
• Research institutions and academia: research assistantships or progression to doctoral study.
• Public sector and NGOs: epidemiological modelling, public‑health informatics and policy‑oriented data analysis.

Why study at Brandeis University

Brandeis offers an intimate, research‑intensive environment with strong interdisciplinary collaboration across biology, computer science, mathematics and chemistry. Students benefit from access to faculty who are active in computational biology research, modern computing facilities, and wet‑lab resources for integrative projects. The university’s location near Boston and the greater biotechnology cluster provides opportunities for partnerships, internships and networking with research institutes, hospitals and industry employers.

Small cohort sizes support close mentorship, while a curriculum that emphasises both theoretical foundations and applied projects helps graduates develop immediately transferable skills. Career services and faculty connections further assist students in transitioning to industry roles or competitive doctoral programmes.