University of Memphis

What you'll study

The programme emphasises quantitative and computational approaches to biological problems. Core themes include mathematical modelling of biological systems, statistical analysis of high-throughput data, algorithm development for sequence and structural analysis, and machine learning applications in biology.

• Core modules — mathematical methods for biology, biostatistics and experimental design, algorithms for bioinformatics, and computational systems biology.
• Programming and tools — coursework and practical training in programming languages commonly used in the field (for example R and Python), data manipulation, reproducible research practices, and use of bioinformatics libraries and pipelines.
• Genomics and transcriptomics — sequence analysis, genome assembly, variant calling and functional annotation, plus analysis of RNA-seq and other omics datasets.
• Machine learning and data mining — supervised and unsupervised methods applied to biological datasets, feature selection, model evaluation and interpretation.
• Mathematical modelling — differential-equation and agent-based models for population dynamics, signalling pathways and epidemiological processes.
• Electives and special topics — structural bioinformatics, computational neuroscience, proteomics, network biology, or advanced statistical genomics depending on faculty offerings.
• Research project or thesis — a substantial research project working with faculty across biology, mathematical sciences or computer science; projects typically involve data analysis, algorithm development or model construction and validation.
• Practical training — opportunities for laboratory rotations, internships or collaborative projects with local biomedical organisations and campus research centres to gain hands-on experience with real datasets and applications.

Entry requirements

Applicants are expected to hold a relevant bachelor’s degree in mathematics, statistics, computer science, engineering, molecular biology, biochemistry or a related discipline. Evidence of quantitative preparation (calculus, linear algebra, probability/statistics) and programming experience is important.

• Academic transcripts demonstrating a strong undergraduate record.
• A statement of purpose outlining research interests and career goals.
• Letters of recommendation from academic or professional referees familiar with the applicant’s quantitative and/or laboratory skills.
• International applicants must demonstrate English language proficiency through recognised tests or institutional waivers.
• Additional materials such as a CV and examples of relevant coursework or coding projects are often recommended. GRE scores may be considered where applicable but are not universally required; applicants should consult the programme for current policy.

Career prospects

Graduates gain strong quantitative and computational skills that are in demand across academia, industry and the public sector. Career paths include roles such as:

• Bioinformatics scientist or genomic data analyst in biotechnology and pharmaceutical companies.
• Computational biologist in research institutes, healthcare organisations or national laboratories.
• Data scientist or machine learning engineer working on biological or clinical datasets.
• Biostatistician supporting clinical trials and translational research.
• Research scientist or PhD candidate pursuing advanced study in systems biology, computational neuroscience, population genetics or related fields.
• Positions in public health agencies, diagnostics companies and startups focused on personalised medicine and biomedical data solutions.

Why study at University of Memphis

The University of Memphis offers an interdisciplinary environment that brings together faculty from mathematical sciences, computer science and biological sciences, providing broad expertise for computational biology training. Students benefit from hands-on research opportunities, collaborative projects with campus research centres and connections to the Memphis biomedical community.

The programme provides mentorship from faculty actively engaged in computational and experimental research, practical experience with real-world datasets, and access to institutional research facilities and high-performance computing resources. Its flexible curriculum supports both students aiming for immediate industry roles and those preparing for doctoral study.