University of Chicago

What you'll study

This PhD emphasises quantitative theory and computational practice applied to molecular, cellular, organismal and ecological problems. Coursework and research typically cover mathematical modelling of biological systems, statistical inference for high‑throughput data, machine learning methods for genomics and imaging, algorithm design for sequence and network analysis, and stochastic processes in population and evolutionary biology.

Typical taught modules and topics include:

• Probability and stochastic processes applied to population dynamics and biochemical kinetics.
• Statistical inference and multivariate methods for analysing genomic, transcriptomic and proteomic datasets.
• Machine learning and computational algorithms with emphasis on scalable methods for large biological datasets.
• Computational genomics and sequence analysis including alignment, assembly and variant calling frameworks.
• Systems and network biology covering regulatory networks, signalling models and dynamical systems.
• Imaging analysis and spatial statistics for microscopy and spatial transcriptomics.

Programme structure is research‑centred: students take a core set of quantitative and biology electives in the first years, complete rotations or short collaborative projects to identify a thesis lab, pass qualifying or candidacy examinations, and then focus on independent dissertation research. Training emphasises coding, reproducible workflows, data management, and communicating results to both quantitative and experimental audiences.

Entry requirements

Applicants are expected to hold a bachelor’s degree or equivalent in a quantitative or life science discipline. Competitive applicants typically have substantial preparation in mathematics (calculus, linear algebra, probability), statistics, and programming; coursework in molecular or cellular biology is advantageous for applicants from quantitative backgrounds.

Required application elements typically include:

• Transcripts demonstrating relevant undergraduate/graduate coursework.
• A personal statement outlining research interests and fit with faculty and resources at the university.
• Letters of recommendation from academic or research supervisors who can speak to research potential.
• Evidence of programming and quantitative skills (coursework, projects or research experience).

The programme evaluates applicants holistically; published research, substantial laboratory or computational research experience, and clear alignment with potential faculty mentors strengthen applications. Standardised tests may be optional or considered on a case‑by‑case basis according to current admissions policies.

Career prospects

Graduates leave well prepared for a range of careers that require deep quantitative and biological expertise. Common paths include:

• Academic research and teaching positions in computational biology, bioinformatics, systems biology and related fields.
• Industry roles in biotechnology, pharmaceutical companies and computational genomics startups (research scientist, data scientist, machine learning engineer focused on biology).
• Positions at national laboratories and research institutes working on big‑data biology, imaging or modelling projects.
• Translational and clinical research roles that combine computational analysis with biomedical data interpretation.
• Scientific consulting and leadership roles in organisations that bridge quantitative methods and the life sciences.

Why study at University of Chicago

The University of Chicago offers a research environment that fosters close interaction between quantitative scientists and experimental biologists. Students benefit from access to leading faculty across departments such as computer science, statistics, ecology & evolution, human genetics and molecular engineering, and from close collaborations with the Pritzker School of Medicine and affiliated research centres.

Resources that strengthen training include interdisciplinary seminars and reading groups, shared computational infrastructure and core facilities, and opportunities to collaborate with nearby national laboratories and partner institutions. The programme’s emphasis on rigorous quantitative training paired with biological application prepares graduates to address contemporary biological problems with both theoretical insight and practical computational skill.