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Interdisciplinary training in biomedical data analysis

Biomedical research generates large volumes of complex data, encompassing genomics, proteomicsmetabolomicsphenotypic data, epidemiology and clinical trial investigations, to inform our understanding of disease mechanisms.

This programme will equip the next generation of scientists with the skills to utilise these data sets in the development of precision medicine. These data impact on the creation of new tailored therapies, aiding earlier diagnosis and the selection of optimal treatment regimens for patients who may be suffering from disorders ranging from common disorders of complex aetiology (such as cancer or cardiovascular disease) to the more rare Mendelian disease (such as muscular dystrophy).

This distinctive programme has been designed to directly meet research needs in industry and academia where there is a need for scientists with both biological knowledge and the computational, statistical and analytical skills to drive genomic precision medicine.

Cutting-edge data science research at Leeds

The University of Leeds is now a partner of The Alan Turing Institute, the UK’s prestigious national institute for data science. The partnership is spearheaded by the Leeds Institute for Data Analytics (LIDA) with contributions from researchers and data scientists across the University. Research in LIDA sheds new light on health and human behaviour and the action needed to tackle a wide range of medical, social and environmental problems.

Our students will benefit from a research-led education at the forefront of the most cutting-edge data analytics research at Leeds.

LIDA is supported by national programmes such as the MRC’s Medical Bioinformatics who invested £5.7 million to enable ground-breaking and productive bioinformatics research at the interface between the clinic, health records and high volume molecular and phenotypic data sets.

Leeds has also recently been the recipient of huge investments from funding councils across the UK, such as the recent £10 million investment in artificial intelligence and digital pathology.

Course content

This degree will prepare you for the rapidly expanding and developing field of precision medicine, equipping you with the diverse skillset required to work in this data-centric discipline.

The modules on this course will introduce you to the advanced technologies used to generate large scale genomics data and the variety of statistical and computational methods used to analyse these:

  • High throughput sequencing, proteomics and metabolomics
  • Regression, classification and random forests
  • Genome-wide genotyping and bioinformatics

Highly interdisciplinary learning and teaching

You'll have access to the very best learning resources and academic support during your studies. We’ve been awarded a Gold rating in the Teaching Excellence Framework (TEF, 2017), demonstrating our commitment to delivering consistently outstanding teaching, learning and outcomes for our students.

The programme is highly interdisciplinary and combines academic expertise across three faculties; Biological SciencesMedicine and HealthMaths and Physical Sciences. You will be able to work with real-life data sets generated through our own research.

Some of your lectures will be delivered by external speakers from industry and the NHS who will use case studies to illustrate different approaches to using data analytics in precision medicine. You’ll also receive a solid grounding in a range of transferable skills, valued by employers, including teamwork, project-work, and knowledge of the legal, ethical and professional guidelines that are relevant to research data use.

Our Cancer Biology Module is delivered at St James’s University Hospital campus site which is home to many cancer researchers located in the Wellcome Trust Brenner Building and the Clinical Sciences Building.

Research Project

You will complete a 60 credit research (computational) project where you will gain in-depth experience of the analysis of large-scale biomedical data to address problems related to health and disease. You will be supervised by researchers who are active in the field of genomics and data analytics.

Career opportunities

As outlined in the recent publication ‘Science Industry Partnership: Skills Strategy 2025’, there is a demand for the skills that will be developed on this programme.

Upon completion of this programme you will be well-placed to undertake a PhD in this field. You may also be able to enter into a number of roles within the industry or NHS:

  • Computational biology
  • Data analytics functions
  • Clinical genetics
  • Disease Biologist
  • Clinical Scientist
  • Clinical leadership in genomic medicine

English language requirements

IELTS 6.5 overall, with no less than 6.0 in any single component. For other English qualifications, read English language equivalent qualifications.

How to apply

Documents and information you’ll need:

  • completed online application form (for Taught Postgraduate Study)
  • full CV
  • transcript of degree examination marks achieved to date
  • copy of final degree certificate (if completed)
  • evidence of English language qualification (non-native English speakers only)
  • copy of passport (if you’re an overseas student).
  • completed supporting statement:



Read about paying fees and charges.

For fees information for international taught postgraduate students, read Masters fees.

Scholarships and financial support

If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government. Find out more at Masters funding overview.

Alumni Bursary - If you are a former student of the University of Leeds you may be eligible for a 10% alumni tuition fee bursary

You can also search our postgraduate scholarships database

You can also find information on MoneySavingExpert

Visit the Precision Medicine: Genomics & Analytics page on the University of Leeds website for more details!




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Recipient: University of Leeds

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