University of Chicago

What you'll study

This programme combines rigorous quantitative training with applied biological problems. Core topics typically include mathematical modelling of biological systems, statistical methods for high‑throughput data, algorithms for sequence and structure analysis, machine learning for biological data, and computational systems biology. Coursework balances theory and practice: you will study differential equations and stochastic processes for population and molecular dynamics; statistical inference, Bayesian methods and reproducible data analysis for genomics and transcriptomics; and algorithmic foundations such as graph algorithms, dynamic programming and optimisation that underpin bioinformatics tools.

Typical modules and practical components you can expect:

• Mathematical Modelling of Biological Systems — deterministic and stochastic models, parameter estimation and sensitivity analysis.
• Statistical Genomics and Population Genetics — association testing, sequence variation, population structure and coalescent models.
• Machine Learning for Biological Data — supervised and unsupervised learning, deep learning for imaging and sequence data, model interpretation.
• Algorithms in Bioinformatics — sequence alignment, assembly, phylogenetics, motif finding and complexity considerations.
• Systems and Network Biology — network inference, pathway modelling, multi‑omic integration.
• Computational Laboratory or Capstone Project — team or individual project using real experimental datasets, often in collaboration with faculty research groups or local biotech partners.

The programme emphasises hands‑on experience with programming (Python, R, and relevant C++/Java tools), data management, cloud and high‑performance computing, and best practices in software development and reproducible research. Many students combine coursework with a supervised research practicum or thesis under faculty from departments such as Human Genetics, Computer Science, Statistics, and the Institute for Genomics and Systems Biology.

Entry requirements

Competitive applicants usually hold a bachelor’s degree in mathematics, statistics, computer science, engineering, physics, or a biological science with substantial quantitative coursework. Typical preparation includes:

• Mathematics: multivariable calculus, linear algebra and differential equations;
• Statistics: probability and mathematical statistics or equivalent courses;
• Programming: proficiency in at least one high‑level language such as Python or R and familiarity with data structures and algorithms;
• Biology: undergraduate coursework in molecular biology, genetics or cell biology is advantageous but not always required if quantitative skills are strong.

Applications usually require official transcripts, a CV, a personal statement outlining quantitative background and research interests, and letters of recommendation from academic or professional referees. Standardised tests policies vary by programme and may be optional; applicants should consult the admissions pages for current guidance. International applicants will usually need to demonstrate English language proficiency.

Career prospects

Graduates enter a range of roles across academia, industry and public sector organisations. Common career paths include:

• Computational Biologist / Bioinformatician — analysing genomic, transcriptomic and proteomic datasets in academic labs or biotech and pharmaceutical companies.
• Data Scientist for Life Sciences — applying machine learning and statistics to diagnostics, clinical data and real‑world evidence.
• Research Scientist / Research Engineer — developing algorithms and software for sequence analysis, structural modelling or systems biology.
• Further study — many graduates pursue PhD programmes in computational biology, statistics, computer science or related biomedical fields.
• Industry roles — positions in biotech, pharmaceutical companies, diagnostics firms, and startups, as well as roles in healthcare analytics and public health agencies.

With its quantitative emphasis and practical training, the degree prepares graduates to contribute to multidisciplinary teams that translate biological data into insight and products.

Why study at University of Chicago

The University of Chicago offers a rigorous, research‑intensive environment with strong interdisciplinary links between biological sciences, statistics, computer science and engineering. Students benefit from access to faculty active in genomics, systems biology, machine learning and statistical methodology, and from institutional resources such as shared high‑performance computing facilities and collaborative research centres. The local biotechnology and healthcare ecosystem in Chicago provides opportunities for internships, collaborations and industry engagement, while the university’s emphasis on critical thinking ensures graduates are well prepared for both research and applied careers.