St. John's University

What you'll study

The programme blends coursework and project work to develop skills in mathematical modelling, statistical inference and algorithmic analysis of biological data. Core themes include stochastic and deterministic models of biological systems, statistical genomics, machine learning for biological data, and practical programming for computational biology.

• Mathematical and statistical foundations: differential equations for population and cellular models, stochastic processes, applied linear algebra and advanced statistical inference.
• Computational methods: algorithms for sequence analysis, data structures used in bioinformatics, high‑performance computing and software development in languages such as Python and R.
• Bioinformatics and genomics: next‑generation sequencing analysis, comparative genomics, transcriptomics, variant calling and functional annotation.
• Systems and theoretical biology: network biology, dynamical systems approaches to signalling and metabolic pathways, parameter estimation and sensitivity analysis.
• Machine learning and data science: supervised and unsupervised learning, dimensionality reduction, integrative analysis of multi‑omics datasets, and applied deep learning for biological questions.
• Capstone or thesis project: a substantial research project or applied capstone in collaboration with faculty, often using real biological datasets and emphasising reproducible computational workflows.

The programme typically offers elective options and seminars on topics such as structural bioinformatics, evolutionary modelling, biomedical imaging analysis and ethics in computational biology. Students gain hands‑on experience with commonly used tools and platforms for data analysis and reproducible research.

Entry requirements

Applicants are expected to hold an accredited bachelor’s degree in mathematics, statistics, computer science, biology, biomedical engineering or a closely related discipline. Successful candidates normally demonstrate strong quantitative preparation and some programming experience.

• Academic background: undergraduate training including calculus, linear algebra, introductory statistics/probability and at least one programming course or equivalent practical experience.
• Supporting documents: official transcripts, a statement of purpose describing research and career goals, a current CV or résumé, and academic or professional references.
• English language: international applicants are required to demonstrate English proficiency through an approved test unless exempted by prior study in English.
• Additional consideration: some applicants may be asked to provide examples of quantitative coursework or a portfolio of coding work. Prior coursework in molecular biology or genetics is beneficial but not always mandatory if quantitative skills are strong.

Career prospects

Graduates move into roles that sit at the interface of biology, data and computation. Typical career paths include positions in biotechnology and pharmaceutical companies, academic and government research laboratories, healthcare analytics and public health agencies.

• Bioinformatics scientist or computational biologist in industry and academia
• Data scientist or machine learning engineer focusing on biomedical or genomics data
• Research analyst roles in hospitals, clinical research organisations and public health institutions
• Positions in biotechnology startups, diagnostics companies and precision medicine programmes
• Progression to doctoral study (PhD) in computational biology, systems biology, biostatistics or related fields

Why study at St. John's University

St. John's offers an interdisciplinary environment where mathematicians, biologists and computer scientists collaborate on applied biomedical problems. The programme emphasises small cohort sizes and faculty mentorship, enabling close supervision of research projects and personalised career guidance.

• Faculty and research: access to faculty with expertise across biomathematics, bioinformatics and computational biology and opportunities to join active research projects.
• Location and connections: located in a major metropolitan area with proximity to hospitals, research institutes and biotech companies that offer internship and collaboration opportunities.
• Practical training: strong emphasis on hands‑on data analysis, reproducible computing practices and the development of transferable programming and statistical skills valued by employers.
• Student support: academic advising, career services and workshops on professional skills, grant writing and preparation for further graduate study.

The programme is designed for students seeking a rigorous, applied pathway into computational biology and prepares graduates for research roles, industry positions and continued study at the doctoral level.