Florida State University

What you'll study

This master's combines core quantitative training with domain-specific bioinformatics and computational biology topics. You will study mathematical modelling of biological systems, stochastic processes, and dynamical systems alongside computational methods such as algorithms for sequence analysis, machine learning for biological data, and statistical genomics. Coursework typically covers topics like:

• Mathematical foundations: differential equations, linear algebra, numerical analysis and stochastic modelling applied to biological systems.
• Computational methods: algorithms, data structures, high-performance computing and software development for analysis of large-scale biological datasets.
• Statistical and machine-learning approaches: statistical inference, Bayesian methods, regression, clustering, and supervised learning for genomics, transcriptomics and proteomics.
• Bioinformatics subjects: sequence analysis, comparative genomics, structural bioinformatics, and network biology.
• Systems and theoretical biology: systems biology, metabolic and regulatory network modelling, and multi-scale modelling.
• Practical and research components: programming labs (commonly in Python, R, or MATLAB), data analysis projects, seminars, and a thesis or practicum/research project option.

The programme is organised to let students build a tailored curriculum: a mix of core modules to ensure quantitative rigour, elective modules to specialise (for example in genomics, imaging analysis or epidemiological modelling), and a substantial research or applied project supervised by faculty across mathematics, statistics, computer science and biological sciences.

Entry requirements

Applicants should normally hold a bachelor's degree in mathematics, statistics, computer science, engineering, physics, or a life-science discipline with substantial quantitative coursework. Successful applicants typically have:

• A solid quantitative background: calculus, linear algebra, probability and statistics are expected prerequisites.
• Programming experience in one or more languages commonly used in computational biology (for example Python, R, C/C++ or MATLAB).
• Some exposure to molecular biology or genetics is beneficial for bioinformatics-focused work, though bridging courses are often available.
• Application materials: academic transcripts, a personal statement outlining research interests and goals, a current CV, and two or three academic or professional references.
• International applicants must demonstrate English proficiency through accepted tests unless otherwise exempted by the university.

The programme evaluates applicants holistically, considering academic preparation, relevant experience (research, internships or industry work), and fit with available faculty supervisors. Some applicants with strong quantitative skills but limited biology background may be admitted and advised to take foundational biology modules.

Career prospects

Graduates go on to a range of research and applied careers across academia, industry and the public sector. Common career paths include:

• Bioinformatics scientist or computational biologist in pharmaceutical and biotechnology companies, working on genomics, drug discovery or biomarker development.
• Data scientist or quantitative analyst in healthcare analytics, clinical informatics, or public-health organisations.
• Research scientist or doctoral study in computational biology, applied mathematics, statistics or computer science.
• Positions in government agencies, national laboratories or non-profit research organisations focusing on epidemiology, population genomics or environmental modelling.
• Technology roles in software development for scientific tools, or consulting roles that apply quantitative biology expertise to industry problems.

The combination of computational, statistical and biological skills prepares graduates to handle large biological datasets, design and validate predictive models, and collaborate across multidisciplinary teams.

Why study at Florida State University

Florida State University offers an interdisciplinary environment that brings together faculty from mathematics, statistics, computer science and the biological sciences, enabling collaborative projects and cross-department supervision. Students benefit from access to institutional research infrastructure, including high-performance computing resources and specialised research institutes that support molecular and computational biology work.

The university’s location and research networks provide opportunities for applied projects and internships with regional research centres, healthcare organisations and industry partners. Small-group mentorship, seminar series and practicum options mean students gain both theoretical training and hands-on experience, positioning graduates for research or applied careers in computational biology and related fields.