Georgetown University

What you'll study

The programme combines foundational courses in mathematics, statistics and computer science with applied subjects in molecular and systems biology. Core topics typically include mathematical modelling of biological systems, statistical genomics and population genetics, machine learning for biological data, algorithms for sequence and structure analysis, and methods for analysing high-throughput omics data. Students also study practical skills such as scientific programming (Python/R), database design for biological data, reproducible research, and use of high-performance computing.

• Core modules: mathematical models in biology, statistical inference for bioinformatics, computational methods for genomics.
• Applied modules: systems biology and network modelling, structural bioinformatics and molecular simulation, transcriptomics and single-cell analysis.
• Practical components: programming practicum, data-driven projects, and workshops on software tools and pipelines.
• Capstone options: a supervised research thesis, an industry-style practicum project, or an extended team-based data analysis project.

Entry requirements

Applicants normally hold a bachelor’s degree in a quantitative or life-science discipline such as mathematics, statistics, computer science, biology, biochemistry, physics, or engineering. Strong preparation in calculus and linear algebra, basic probability and statistics, and some programming experience (for example Python or R) is expected. Prior coursework in molecular biology or genetics is advantageous for applied modules.

• Academic transcripts from undergraduate study demonstrating solid quantitative and/or biological preparation.
• A personal statement describing research interests, relevant experience and career goals.
• Curriculum vitae or résumé highlighting technical skills and research or project experience.
• Two or three letters of recommendation from academic or professional referees who can speak to the applicant’s quantitative and research potential.
• Proof of English proficiency for applicants whose first language is not English, where required by the university.

Career prospects

Graduates are prepared for careers that use computational approaches to solve biological and biomedical problems. Typical roles include computational biologist, bioinformatics scientist, data scientist in healthcare or biotech, genomics analyst, and research associate in academic or industry laboratories. The programme also prepares students for advanced research degrees (PhD) in computational biology, biostatistics or related fields.

• Employment sectors: biotechnology and pharmaceutical companies, clinical and hospital laboratories, public-health agencies, contract research organisations, and academic research groups.
• Skills employers seek: statistical modelling of biological data, pipeline development for sequencing and omics, machine learning for predictive models, and reproducible computational research.
• Local opportunities: proximity to federal agencies, research institutes and a vibrant biomedical community provides internships and collaborative research possibilities in the Washington, D.C. and Baltimore research corridor.

Why study at Georgetown University

Georgetown offers an interdisciplinary environment that links strong quantitative departments with biomedical research centres. Students benefit from access to faculty actively working at the interface of computation and biology, collaborative research groups, and institutional resources such as high-performance computing and core sequencing facilities.

• Interdisciplinary training with opportunities to take courses and collaborate across departments, enabling personalised study plans that emphasise theory, methods or applied practice.
• Research connections to medical and biomedical units on campus, providing options for thesis projects and access to clinical and translational research questions.
• Strategic location in Washington, D.C., offering networking, internships and exposure to policy and regulatory perspectives on biomedical data and ethics.
• Small cohort sizes and close faculty supervision that support hands-on training, project mentorship and career advising tailored to computational biology careers.