Rochester Institute of Technology

What you'll study

This master's blends coursework in mathematical modelling, statistics and algorithms with laboratory-informed bioinformatics and computational biology practice. Core themes include sequence analysis and genomics, statistical methods for biological data, machine learning for biological discovery, systems and network modelling, structural bioinformatics and algorithm design for bioinformatics.

• Core modules: mathematical and statistical foundations (probability, inference), numerical methods, computational modelling of biological systems, and algorithmic approaches to sequence and structure analysis.
• Applied modules: next-generation sequencing analysis, comparative genomics, transcriptomics and proteomics analysis, structural prediction and molecular dynamics, and bioinformatics pipelines.
• Computing and data science: programming for bioinformatics (Python, R), high-performance computing and cloud workflows, machine learning and data visualisation tailored to biological datasets.
• Research and practice: a research project or thesis with faculty, and options for a practicum/industry project that apply computational methods to real biological or biomedical datasets.

Programme delivery emphasises hands-on data analysis, reproducible research practices and interdisciplinary collaboration between computational scientists and experimentalists.

Entry requirements

Applicants are normally expected to hold a bachelor's degree in biology, mathematics, statistics, computer science, engineering or a closely related field, with evidence of strong quantitative preparation. Typical preparation includes coursework in calculus, linear algebra, probability and statistics, introductory programming, and foundational molecular biology or genetics.

• Academic transcripts: official transcripts demonstrating relevant undergraduate training.
• Supporting documents: a statement of purpose describing research interests and goals, a current CV or résumé, and letters of recommendation.
• English language proficiency: required for international applicants who have not completed prior study in English; acceptable test scores or institutional waivers are considered.
• Additional preparation: applicants with limited background in either biology or computing may be admitted with the expectation they complete prerequisite bridge courses before or during the programme.

Career prospects

Graduates move into a broad range of roles where computation and life sciences intersect. Employment destinations include biotechnology and pharmaceutical companies, clinical and translational research groups, agricultural and environmental genomics organisations, health‑data start-ups, and technology firms providing bioinformatics solutions.

• Common job titles: computational biologist, bioinformatician, data scientist for biology, biostatistician, genomics analyst, and research scientist.
• Work settings: industry research and development, academic and government laboratories, clinical and diagnostic companies, contract research organisations and software vendors developing bioinformatics tools.
• Further study: many graduates continue to doctoral programmes in computational biology, bioinformatics, biostatistics or related disciplines.

Why study at Rochester Institute of Technology

Rochester Institute of Technology offers an environment suited to interdisciplinary computational life‑science training, combining strong computing and engineering resources with collaborations across biological and biomedical research groups. Students benefit from access to high-performance computing facilities, well-equipped laboratories, and faculty who work on applied bioinformatics and systems biology problems.

• Hands-on and industry-relevant experience: the institute’s emphasis on experiential learning and its established co‑op and industry partnership culture provide opportunities to undertake practicum projects and internships with biotechnology, healthcare and technology partners.
• Interdisciplinary collaborations: close links between departments allow students to work alongside researchers in computational science, biology, data science and engineering on cross-cutting projects.
• Research and facilities: access to computational infrastructure and laboratory resources supports thesis and project work that tackles large-scale biological data and modelling challenges.