University of Nottingham

What you'll study

This is a research doctorate focused on an original project supervised by academic staff across computational and life-science disciplines. Projects typically sit at the intersection of genomics, systems biology, structural bioinformatics, population genetics, medical imaging analysis or single‑cell and spatial transcriptomics. The programme emphasises hypothesis-driven research, algorithm and software development, and quantitative analysis of biological data.

• Core research activity: an independent research project leading to a thesis and viva; projects commonly involve large-scale data analysis, model development or integration of computational and experimental methods.
• Methods and techniques: statistical inference and applied probability, machine learning for biology, network and systems modelling, sequence analysis, phylogenetics, structural modelling, and high-performance or cloud computing workflows.
• Skills training: software engineering for reproducible research (version control, testing, containers), data visualisation, scientific communication and research ethics.
• Optional coursework and modular training: short courses in advanced statistics, programming (R, Python), bioinformatics pipelines, imaging analysis, and domain-specific workshops depending on the student’s project and training needs.
• Progression: regular supervisory meetings, annual reviews or milestones, and presentation of work at group meetings and conferences before submission of the thesis for examination.

Entry requirements

Applicants are expected to hold a strong undergraduate degree (typically a 2:1 or equivalent) in a relevant discipline such as bioinformatics, computational biology, biology, computer science, mathematics, statistics, physics or engineering. A relevant postgraduate qualification (MSc or equivalent) with research experience is highly desirable and may be required for some projects.

• Essential skills: demonstrable programming ability (for example in Python or R), familiarity with statistical concepts, and experience of analysing biological data or developing computational models.
• Research experience: evidence of independent research (project, dissertation, publications or technical reports) strengthens an application; applicants should outline a research interest or proposal and identify potential supervisors where possible.
• References: two academic references that can attest to the applicant’s research potential and quantitative skills.
• English language: applicants whose first language is not English must meet the University’s English language requirements; details are available from the admissions pages.
• Funding: applicants can apply with their own funding, or for competitive studentships and scholarships; selection for funded studentships may include additional eligibility criteria.

Career prospects

Graduates from a Computational Biology PhD have a broad range of career options. Many continue in academic research as postdoctoral researchers and lecturers, while others move into research scientist roles in industry or the public sector.

• Academic pathways: postdoctoral positions, research fellowships, and academic roles in departments of biology, bioinformatics, computer science and related fields.
• Industry roles: data scientist, bioinformatician, computational biologist or machine-learning engineer in pharmaceutical, biotechnology and genomics companies.
• Healthcare and public sector: clinical data analytics, genomics services, NHS informatics roles, and positions in public-health agencies.
• Other careers: research software engineering, scientific consulting, regulatory science, science policy and technology transfer or start-up ventures based on computational and genomic technologies.

Why study at University of Nottingham

The University of Nottingham offers an interdisciplinary environment with access to expertise across life sciences, medicine and computing, enabling computational biology students to develop collaborative projects with experimental groups and clinical partners. The university supports doctoral training through structured skills programmes, regular seminars and a research-led culture that fosters collaboration.

• Research environment: supervisors with active research portfolios in genomics, systems biology, bioinformatics and machine learning, and opportunities to work alongside wet‑lab and clinical researchers.
• Facilities: access to high-performance computing resources, core genomics and imaging platforms, and computing infrastructure for scalable data analysis and reproducible workflows.
• Support and training: postgraduate researcher development, grant-writing and career workshops, and support for conference attendance and publication.
• Collaborations: links with industry, translational centres and healthcare organisations provide routes to impact, internships and collaborative funding opportunities.