Cranfield University

What you'll study

The programme combines taught modules on core computational, statistical and biological concepts with practical hands‑on training and a substantial research project. Typical module topics include:

• Programming for bioinformatics – practical use of scripting languages (commonly Python and R), software development practices and reproducible workflows.
• Computational genomics – sequence alignment, assembly, variant calling, population genomics and genome annotation.
• Transcriptomics and functional genomics – RNA‑seq analysis, differential expression, single‑cell data concepts and pathway analysis.
• Proteomics and structural bioinformatics – protein sequence analysis, mass spectrometry data handling and basic structural interpretation.
• Statistical methods and machine learning – applied statistics, data visualisation and machine learning techniques tailored to biological datasets.
• Data management and high‑performance computing – database design, workflows, cloud and cluster computing for large‑scale data processing.
• Research project – an independent dissertation project that applies methods learnt to a real dataset; projects may be laboratory‑linked, industry‑sponsored or computationally focused.

Teaching is delivered through lectures, computer practicals, group work and independent study. Assessment typically combines coursework, laboratory reports, practical exercises and the final project dissertation.

Entry requirements

Applicants normally hold a UK undergraduate degree at honours level (or an international equivalent) in a relevant subject such as biological sciences, biomedical science, computational biology, mathematics, statistics, computer science, or engineering. Applicants with a lower degree classification may be considered if they have substantial relevant work experience or demonstrable skills in programming and quantitative analysis.

It is advantageous to have prior exposure to molecular biology concepts and some experience of programming or statistics; where these are lacking, pre‑course self‑study or bridging modules can help. All applicants whose first language is not English will need to demonstrate proficiency in academic English in line with UK postgraduate admissions requirements.

Career prospects

Graduates are equipped for roles that require handling and interpreting biological data. Typical career paths include:

• Bioinformatics analyst or computational biologist in pharmaceutical and biotechnology companies.
• Genomic data scientist in clinical diagnostics, healthcare organisations and genomic medicine projects.
• Research positions in academic institutes or government laboratories working on genomics, proteomics or systems biology.
• Data science or software development roles in companies providing bioinformatics tools, digital health platforms or agricultural biotech.
• Progression to PhD study in bioinformatics, computational biology or related interdisciplinary areas.

The programme’s applied focus and project component help students build a portfolio of practical skills and examples that are attractive to employers across industry and research sectors.

Why study at Cranfield University

Cranfield is a postgraduate‑only university with a strong emphasis on applied research and industry collaboration. The Applied Bioinformatics MSc benefits from multidisciplinary teaching and access to computing infrastructure and specialist facilities used for real‑world problem solving. Students frequently undertake industry‑linked projects and benefit from the university’s strong employer network, professional development support and career services focused on transitioning graduates into technical roles.

The course is designed for practical skill development, combining technical training in programming, data analysis and high‑performance computing with experience of managing and delivering a substantial project—preparing graduates to contribute effectively in both commercial and research settings.