Virginia Commonwealth University

What you'll study

The programme combines coursework, research rotations and an independent dissertation to deliver advanced training in mathematical modelling, algorithm development and large-scale biological data analysis. Core topics typically include:

• Mathematical and statistical foundations: differential equations, stochastic processes, statistical inference, Bayesian methods and multivariate statistics.
• Computational methods and software: algorithm design, high-performance computing, numerical methods, software engineering for scientific code and reproducible research practices.
• Bioinformatics and data science: sequence analysis, genomics, transcriptomics, proteomics, population genetics and machine learning for biological data.
• Systems and theoretical biology: dynamical systems models of cellular processes, network biology, infectious disease modelling and quantitative systems pharmacology.
• Domain-specific applications: structural bioinformatics, imaging analysis, cancer biology, immunology and translational bioinformatics in collaboration with clinical researchers.

Programme structure normally includes an initial period of coursework and laboratory rotations to identify a dissertation advisor, a qualifying or comprehensive examination, development of a dissertation proposal, and several years of research culminating in a written dissertation and oral defence. Students are expected to present work at seminars and conferences and to publish peer‑reviewed research as part of degree progression.

Entry requirements

Applicants should hold a relevant undergraduate degree (for example in mathematics, statistics, computer science, engineering, physics, or biology) and a strong record of quantitative coursework; a master’s degree in a related field is commonly held by admitted students but not strictly required. Typical application materials include academic transcripts, a statement of purpose outlining research interests and fit with potential faculty advisors, a curriculum vitae, and at least two academic references.

Applicants are expected to have programming experience (for example Python, R, MATLAB or C/C++) and coursework or demonstrated competence in calculus, linear algebra and probability/statistics. Prior research experience in a computational or laboratory setting is strongly recommended. International applicants must satisfy English language proficiency requirements.

Career prospects

Graduates are prepared for research careers in academia, industry and government. Common career paths include:

• Academic research positions and postdoctoral fellowships in computational biology, systems biology, bioinformatics or applied mathematics.
• Research scientist and data scientist roles in biotechnology, pharmaceutical and diagnostics companies, working on genomics, drug discovery, clinical bioinformatics and precision medicine.
• Quantitative analyst and modelling roles in public health agencies, healthcare systems and policy organisations, including infectious disease modelling and epidemiology.
• Software engineering and computational infrastructure roles supporting scientific computing, core bioinformatics facilities and high‑performance computing centres.

The training emphasises both methodological development and applied collaborative research, equipping graduates to translate quantitative approaches into biologically and clinically relevant outcomes.

Why study at Virginia Commonwealth University

VCU offers a collaborative, interdisciplinary environment that brings together faculty from biology, mathematics, computer science, engineering and the School of Medicine. Students benefit from access to clinical collaborators at the VCU Health system and research centres such as the Massey Cancer Center, facilitating translational projects and access to diverse biomedical datasets.

The university provides shared core facilities and computing resources, opportunities to engage with local biotech and healthcare partners in Richmond, and a graduate community that emphasises collaborative mentoring and professional development. Faculty research strengths span theoretical modelling, machine learning for biological data, genomics, imaging analytics and translational bioinformatics, giving students a wide range of potential dissertation topics and cross-disciplinary supervision.