MSc Biomedical Data and Artificial Intelligence
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | A 2:1 degree or equivalent |
|---|---|
Why study MSc Biomedical Data and Artificial Intelligence at Exeter?
- Develop skills in data science and AI motivated by current research in biology and medicine
- Learn about mechanistic mathematical modelling and dynamical systems theory applied to biology, ecology, neuroscience, synthetic biology and medicine complemented by advanced data science methods
- You’ll keep abreast of current research happening in this fast-moving field from a range of internal and guest speakers our regular seminar series
- Become proficient in requisite computational tools and techniques with practical sessions including key industry-standard programming languages such as MATLAB and Python.
- Undertake an independent research project where you’ll explore your interests in greater depth, with the support of an academic, where you can focus on or contribute to current research.
- Interact with world-leading research groups at the Living Systems Institute and gain first-hand experience of interdisciplinary study, including performing biological experiments and analysing biological datasets
Fast Track (current Exeter students)
Professor Krasimira Tsaneva Atanasova talks about Biomedical Data and Artificial Intelligence at the University of Exeter. (This video refers to the mathematical modelling component of the course.)
Top 20 in the UK for Mathematics
20th in The Times and The Sunday Times Good University Guide 2026 and the Complete University Guide 2027
Top 10 in the world for our Mathematics and Computer Science research
CWTS Leiden Ranking 2024, by percent of articles in the top 10% most-cited
Wide range of exciting and high-impact research projects
Research expertise in mathematics for healthcare; systems biology; control and dynamics and computational neuroscience
Entry requirements
Normally a 2:1 Honours degree or equivalent in a mathematics, science or engineering subject, with significant mathematics content.
Requirements for international students
If you are an international student, please visit our international equivalency pages to enable you to see if your existing academic qualifications meet our entry requirements.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course.
The required IELTS test scores for this course fall under Profile B1.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
The modules below provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Please note that the module information displayed here is subject to change.
135 credits of compulsory modules, 45 credits of optional modules
You may select 30-45 credits form Optional Module Group 1
You may select 0-15 credits from Optional Module group 2
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Mathematical Biology and Ecology | 15 | |
| Advanced Topics in Mathematical and Computational Biology | 15 | |
| AI and Data Science Methods for Life and Health Sciences | 15 | |
| Advanced Mathematics Project | 60 | |
| Research in Mathematical Sciences | 15 | |
| Mathematical Modelling in Biology and Medicine | 15 | |
MTH3006: Mathematical Biology and Ecology
This module provides an opportunity to explore how mathematics can be applied to the biosciences to quantitatively model biological processes, ranging from molecular processes within living cells to population-level behaviour and demographic phenomena. The material is designed to give a broad overview of the role that applied mathematics plays across the biological sciences.
You will develop, analyse, and interpret mathematical models (typically formulated as differential equations or iterated maps) using real-world examples from nature. Topics studied may include the population dynamics of insects, animals, or fish; competitive exclusion between species; the kinetics of chemical reactions that power living cells; and mechanisms of biological pattern formation arising from reaction–diffusion equations
MTHM009: Advanced Topics in Mathematical and Computational Biology
This course will cover mathematical and computational approaches that are widely used in current research on a range of topics investigating dynamics in biology, including neuroscience. The dynamical phenomena covered in this course, such as bi-stability, oscillations and synchronisation occur in a wide range of biological systems. Mathematical approaches such as bifurcation and network analysis will be taught alongside their computational counterparts relying on the implementation of robust numerical methods. The module will be delivered through a combination of lectures and computer lab sessions.
This module is recommended for mathematical-biology track students.
Pre-requisite modules: MTH3039 or MTH3006
Co-requisite modules: NSCM006 or MTHM018
This module will build upon methods used to study dynamical systems from related 3rd year and M-level modules, including bifurcation and network analyses. These approaches will be taught alongside their computational counterparts for simulating dynamical systems models and performing numerical bifurcation analysis using continuation methods. Students will also gain skills in the biological interpretation and implications of results stemming from analysis of biological and biomedical models.
MTHM015: AI and Data Science Methods for Life and Health Sciences
Analysing data and quantitatively comparing mathematical models to data are crucial when using mathematics to improve our understanding of complex biological systems. Data from biology experiments and clinical recordings are diverse and often present challenges for analysis and modelling, such as high dimensionality and non-stationarity. This module will introduce you to some common kinds of data observed in biological and clinical applications such as images, time series and high dimensional sequencing data. You will be introduced to advanced methods that deal with these data, but can also be applicable in other fields, for example in climate systems and finance.
Competence in a scientific programming language (such as Matlab or Python) is highly desirable
We will introduce data and methods that arise in the above application areas but are also applicable in other fields. The content will be centred on real-world applications: for example, the analysis of motility in single cell organisms, analysis of clinical time series in neurology and neuroendocrinology as well as analysis of next generation sequencing (NGS) data.
The study of these examples will require theory in:
MTHM021: Advanced Mathematics Project
In this module, you will gain experience of independent work by conducting in-depth research into an open-ended problem agreed with your Project Supervisor. A Good Research Practice portfolio (20%) recognises your professionalism and research performance, and your participation in the wider research, academic, or professional environment associated with your project. Partway through the project, you will present your work to date to a group of academics and fellow students in order to obtain feedback on your progress and to help guide the remainder of your study. This forms 10% of the assessment. At the end of the project, you will write a coherent and comprehensible account of your work in the form of a dissertation that forms 70% of the assessment. Computational work using appropriate software may form part of the Advanced Mathematics Project.
This module provides the opportunity for you to produce a well-researched project in mathematics, either complementing or extending material in the taught part of the programme. By taking this module, you will develop research skills, including literature review, problem formulation, analysis, and communication of mathematical results.
You will receive guidance on the responsible use of AI-assisted tools to support aspects of your research, report writing and presentation preparation, in line with University policy.
MTHM036: Research in Mathematical Sciences
This is a unique module that runs in several research themes and provides you with a taste for and experience in research in mathematical sciences. The themes are broadly aligned with Fourth Year options and would cover subjects from pure maths, applied maths and statistics, although the number and range of topics may change from year to year. Each theme consists of lectures, student-led discussion, reading, and practical sessions, getting deeper experience in research approaches and skills. Students will learn to write a short essay, make an oral presentation and write a longer scientific report.
Set in at least three distinct research themes, this module will introduce you to scientific thinking and abstraction and give you knowledge/experience in core research skills: reading (and understanding), writing and presenting for the mathematical sciences.
NSCM005: Mathematical Modelling in Biology and Medicine
This is an advanced module in mathematical modelling applied to biology and medicine that focuses on modern applications of mathematical techniques to cutting-edge research in these areas. It will introduce you to advanced topics in biochemical networks, physiology, neuroscience and biomedical data analysis. The module is run as a combination of lectures and hands-on computational modelling sessions, and may also involve laboratory visits.
This module provides you with small-group teaching across a selection of advanced topics, reflecting the research interests of the staff involved. The syllabus consists of several short courses, each taught as a self-contained set comprising 1 hour-long lectures together with 2 hours-long workshops/tutorials per week. In order to take this module, you must ensure that you have completed module MTH2003.
This is an optional module for Final Year students of MSci Natural Sciences, and is also an optional module for Final Year Mathematics, Computer Science and Physics undergraduates.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Fractal Geometry | 15 | |
| Mathematical Theory of Option Pricing | 15 | |
| Advanced Topics in Mathematical and Computational Biology | 15 | |
| Representation Theory of Finite Groups | 15 | |
| AI and Data Science Methods for Life and Health Sciences | 15 | |
| Advanced Topics in Statistics | 15 | |
| Dynamical Systems and Chaos | 15 | |
| Fluid Dynamics of Atmospheres and Oceans | 15 | |
| Modelling the Weather and Climate | 15 | |
| Algebraic Curves | 15 | |
| Magnetic Fields and Fluid Flows | 15 | |
| Statistical Modelling in Space and Time | 15 | |
| Space Weather and Plasmas | 15 | |
| Bayesian Statistics, Philosophy and Practice | 15 | |
| Ergodic Theory | 15 | |
| Fundamentals of Weather and Climate Science | 15 | |
| Mid-latitude Weather Systems | 15 | |
| Climate Change Science and Solutions | 15 | |
| Topics in Analytic Number Theory | 15 | |
| Quantitative Methods and AI for Environmental Challenges | 15 | |
| Applications of Data Science and Statistics | 15 | |
| Optional 2 | ||
| Advanced Geotechnical Engineering | 15 | |
| Agile, Lean and Competitive Enterprise | 15 | |
| Multivariable State-Space Control | 15 | |
| Advanced CFD | 15 | |
| Structural Design | 15 | |
| Conceptual Design of Buildings | 15 | |
| Sustainable Engineering | 15 | |
| Nature-Inspired Computation | 15 | |
| Research Methodology | 15 | |
| Machine Learning | 15 | |
| Evolutionary Computation and Optimisation | 15 | |
| Computer Modelling and Simulation | 15 | |
| Computer Vision | 15 | |
| Introduction to Data Science | 15 | |
| Fundamentals of Data Science | 15 | |
| Learning from Data | 15 | |
| Social Networks and Text Analysis | 15 | |
| Stochastic Processes | 15 | |
| Machine Learning (Professional) | 15 | |
| High-Performance Computing | 15 | |
| Fundamentals of Security | 15 | |
| Building Secure and Trustworthy Systems | 15 | |
| Security Assessment and Validation | 15 | |
| Number Theory | 15 | |
| Fluid Dynamics | 15 | |
| Partial Differential Equations | 15 | |
| Applied Differential Geometry | 15 | |
| Mathematics: History and Culture | 15 | |
| Graphs, Networks and Algorithms | 15 | |
| Stochastic Processes | 15 | |
| Cryptography | 15 | |
| Statistical Inference | 15 | |
| Mathematics of Climate Change | 15 | |
| Galois Theory | 15 | |
| Computational Nonlinear Dynamics | 15 | |
| Topology and Metric Spaces | 15 | |
| Integral Equations | 15 | |
| Statistical Computing | 15 | |
Please note that the module information displayed here is subject to change.
- Computer-in-the-loop control of cellular population dynamics (Kyle Wedgwood)
- Pattern formation via long-range cell-to-cell contact: revisiting Turing's morphogenesis hypothesis (Kyle Wedgwood)
- Network modelling approaches to generating seizure onset patterns (Jen Creaser)
- Mathematical modelling and analysis of brain dynamics in mood disorders (Jen Creaser)
- Mathematical modelling and analysis of antibiotics uptake in gram negative bacteria and implications for antimicrobial resistance (Krasi Tsaneva-Atanasova)
- Mathematical modelling for precision medicine (Krasi Tsaneva-Atanasova)
- The nonlinear neural dynamics of synchronising to complex rhythms (James Rankin)
- The impact of hearing loss on language networks (James Rankin)
- A mathematical and computational framework for neurobiological modelling: Behaviour-driven optimisation of neural connectivity (Roman Borisyuk)
- What does that neuron do? A study of the neural circuits that produce swimming in the tadpole (Roman Borisyuk)
- Dynamics of locomotion and phagocytosis in shape-changing cells: theory and experiments (Kirsty Wan)
- A novel opto-hydrodynamical platform for studying microorganism movement in three-dimensions (Kirsty Wan)
- Modelling convergent cell signalling pathways mediating the neurophysiological stress response (Jamie Walker)
- Modelling the relationship between ion channel expression and electrical activity in stress-sensitive cells (Jamie Walker)
- Linking models of large-scale brain networks with data to understand neurological disorders (Marc Goodfellow)
- Personalised brain models for the management of epilepsy (Marc Goodfellow)
The staff are really friendly and the lecturers are really approachable. The course is really interesting and whatever topic I have wanted to learn, we have done at Exeter.
Mathematical Biology was my favourite subject as an undergraduate, so when Covid cut my travels short, my initial plans to start working changed direction and I realised that I wanted to continue my studies. Exeter’s programme seemed exciting and innovative and I thought it would be the best fit for me. Despite the challenges provided by Covid, there were still plenty of opportunities to meet my course mates - both in person and online, and I have made great friends who share my passion and interest in this subject.
The course was seriously hard work, but thanks to that I ended up gaining so much. Before I started, I really struggled with programming and would always try to avoid it, but by the end, I felt confident in Python and MATLAB, even choosing to go on to do a technical consultancy grad scheme. As part of the MSc we were given lots of opportunities to engage with senior members of the maths department, participating in their reading groups and becoming comfortable in the world of academia/research. Our lecturers and supervisors gave us unending support throughout the course, while also allowing us to work and think more independently. I have felt myself grow both as a mathematician and as a person during my MSc.
Jess
MSc in Mathematical Modelling in Biology and Medicine
Fees
2026/27 entry
UK fees per year:
- £12,900 full-time; £6,450 part-time
International fees per year:
- £28,900 full-time; £14,450 part-time
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
Terms and conditions, including deadlines, apply.
Teaching and research
Data analysis, AI and mathematical modelling are crucial for developing our understanding of living systems and the mechanisms of disease. In turn, the complexity of living systems can inspire the development of new mathematical approaches.
The University of Exeter encourages inter-disciplinary working, and we have a growing team of researchers tackling some of the most important current problems in the field. In particular, we collaborate with experts in biology and medicine in order to use mathematics to build a better understanding of disease, thereby improving diagnosis and treatment.
Research-led teaching
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies in seminars and in the field and you will become actively involved in a research project yourself. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will also be taught by leading industry practitioners.
Assessment
Modules are either assessed by coursework only, or a mixture of coursework and an exam. For detailed information on assessment see the module descriptors in the programme structure.
Careers
Our Biomedical Data and Artificial Intelligence MSc provides students with a broad range of technical and analytical skills relevant to industrial sectors including the pharmaceutical industry, healthcare providers, software engineering and data analytics.
The skills you will acquire in data science, AI and mathematical modelling can be applied in a range of academic or industrial research fields, including epidemiology (spread of disease), neuroscience, translational medicine and synthetic biology (re-designing natural systems).
A Masters course can also lead to further academic study such as a PhD.
Dedicated careers support
You will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen. Please visit the Career Zone for additional information on their services.
