Georgetown University

What you'll study

The PhD curriculum combines advanced coursework, hands‑on computational training and original research. Core topics typically include mathematical modelling of biological systems, statistical methods for high‑throughput data, algorithms for sequence and structural analysis, machine learning for biological data, systems biology and network inference, and computational genomics. Students also take complementary courses in programming (Python, R), numerical methods, probabilistic modelling and experimental design.

Programme structure normally comprises a period of formal coursework and seminars, laboratory rotations or short research placements to identify a PhD advisor, a qualifying examination or equivalent assessment to enter candidacy, and completion of an original dissertation under faculty supervision. Students gain practical experience with large biological datasets, pipelines for next‑generation sequencing, single‑cell analysis, structural modelling and simulation of dynamical systems. Teaching or mentoring responsibilities are usually part of the training.

Entry requirements

Successful applicants usually hold a strong undergraduate degree in a relevant discipline such as biology, mathematics, statistics, computer science, engineering or a related quantitative field; many applicants also hold a relevant master’s degree. Essential preparation includes coursework in calculus, linear algebra, probability and statistics, and programming experience. Prior exposure to molecular biology, genomics or biostatistics is highly advantageous.

Typical application materials requested are academic transcripts, a statement of research interests, curriculum vitae, letters of recommendation that speak to research potential, and examples of relevant coursework or research experience. The programme evaluates applicants on quantitative preparation, research experience and fit with faculty research interests. Standardised test requirements and funding arrangements vary; consult the graduate admissions page for current guidance.

Career prospects

Graduates enter a range of research and leadership roles. Common career paths include postdoctoral research and tenure‑track academic positions, computational biologist or bioinformatician roles in biotechnology and pharmaceutical companies, data scientist positions in health‑tech and diagnostics firms, and research scientist or analyst roles in government agencies and non‑profits. Skills developed—statistical modelling, algorithm development, large‑scale data analysis and interdisciplinary collaboration—are also highly valued in clinical research, translational teams and private‑sector R&D.

Why study at Georgetown University

Georgetown offers an interdisciplinary environment with faculty working at the intersection of quantitative science and biology, and close connections to the university medical centre and clinical researchers. Being in Washington, D.C., provides access to a concentration of federal research agencies, policy organisations, hospitals and biotech companies that can support collaborative projects and internships.

The programme emphasises mentorship and research‑led training, allowing students to pursue projects that range from basic biological modelling to translational bioinformatics. Students benefit from institutional computing resources, seminar series and opportunities to collaborate across departments, enabling a broad and applied training in computational biology suited to academic and industry careers.