University of Massachusetts Amherst

What you'll study

The programme is research-led and highly interdisciplinary. Core areas of study include mathematical modelling of biological systems, statistical genomics and population genetics, algorithmic bioinformatics, machine learning for biological data, network and systems biology, and high-performance computing for large-scale biological datasets. Students take advanced coursework in topics such as differential equations and dynamical systems for biology, statistical inference and Bayesian methods for genomics, algorithms for sequence and structure analysis, computational neuroscience, and stochastic processes in population biology.

Programme structure typically combines formal coursework, graduate seminars, rotating research projects (or early laboratory attachments), a qualifying or preliminary examination to demonstrate readiness for dissertation research, and an independent original research dissertation supervised by one or more faculty advisors. Regular participation in lab meetings, departmental and interdepartmental seminars, and teaching assistantships are common components of training.

• Typical modules and topics: dynamical systems in biology; numerical methods; statistical learning and high-dimensional inference; genomics and transcriptomics analysis; phylogenetics and population genetics; structural bioinformatics; data visualisation and reproducible research.
• Research training: mentored laboratory research, grant-writing and journal-club presentations, computing and data management for reproducible pipelines.
• Facilities and computing: access to institutional core facilities for genomics and imaging and to departmental and university high-performance computing resources for large-scale analyses.

Entry requirements

Applicants are normally expected to hold a strong undergraduate degree in mathematics, statistics, computer science, engineering, or biological sciences, with substantial quantitative preparation; many incoming students hold a master’s degree in a related discipline. Strong preparation in calculus, linear algebra, probability and statistics, and programming is required; prior coursework or research experience in modelling, algorithms or molecular biology is advantageous.

Typical application materials include official academic transcripts, a research-oriented statement of purpose outlining interests and potential advisors, a curriculum vitae, and multiple letters of recommendation from academic or research supervisors. International applicants must satisfy the university's English language requirements. The programme evaluates candidates for research potential and fit with faculty expertise rather than on a single test score.

Career prospects

Graduates go on to careers across academia, industry and the public sector. Common destinations include tenure-track and research-track academic positions, postdoctoral research in computational biology and genomics, data science and machine learning roles in biotechnology and pharmaceutical companies, computational genomics and bioinformatics positions in clinical and diagnostic laboratories, and research or analyst roles in public health, agriculture and environmental agencies. The programme’s quantitative training also prepares graduates for careers in software engineering, scientific consulting, and in start-ups focused on computational life-science applications.

Why study at University of Massachusetts Amherst

UMass Amherst offers a collaborative interdisciplinary environment with faculty drawn from mathematics and statistics, biology, computer science, and engineering, enabling students to pursue cross-disciplinary projects and co-advising arrangements. The university provides access to modern core facilities for genomics and imaging and to substantial computing resources for large-scale data analysis. Students benefit from a research-active campus with frequent seminars, workshops and collaborations, as well as connections to regional research networks and industry partners, supporting both academic and applied career pathways.