MSci Mathematics (Mathematical Biology)
Please note: This page is for 2026 entry. Click here for 2027 entry.
| UCAS code | G103 |
|---|---|
| Duration | 4 years |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | A-Level: AAA-AAB |
|---|---|
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A-Level: ABB-ABC |
Why study MSci Mathematics (Mathematical Biology) at Exeter?
- Develop your passion for mathematics within this important field of research that is crucial to our understanding of living systems
- See how the complexity of living systems can inspire the development of new mathematical approaches
- Learn from and engage with current research being undertaken within the department
- Optional to take the ‘Commercial and Industrial Experience’ module during the vacation before your third year, allowing you to gain paid work experience in a commercial setting while earning credits towards your degree
Discover Mathematics at the University of Exeter.
Top 20 in the UK for Mathematics
20th in The Times and The Sunday Times Good University Guide 2026
92% of Mathematics graduates in or due to start employment or further study fifteen months after graduation
Based on full-time, first degree, UK domiciled graduates, HESA Graduate Outcomes survey 2021/22
Supportive department prioritising contact time between students and staff
This course is accredited to meet the educational requirements of the Chartered Mathematician designation awarded by the Institute of Mathematics and its Applications (IMA).
Entry requirements (typical offer)
| Qualification | Typical offer | Required subjects |
|---|---|---|
| A-Level | AAA-AAB |
GCE A-Level Maths grade A
Candidates may offer GCE A-Level Maths, Pure Maths or Further Maths. |
| IB | 36/666-34/665 | HL6 in Mathematics (Analysis and Approaches) |
| BTEC | DDD | Applicants studying a BTEC Extended Diploma will also require GCE A-Level Maths grade A |
| GCSE | 4 or C | Grade 4/C in GCSE English language |
| Access to HE | 30 L3 credits at Distinction Grade and 15 L3 credits at Merit grade | 15 L3 credits at Distinction Grade in an acceptable Mathematics subject area |
| T-Level | T-Levels not accepted | N/A |
| Contextual Offer | A-Level: ABB-ABC |
Specific subject requirements must still be achieved where stated above. Find out more about contextual offers. |
| Other accepted qualifications | ||
| English language requirements |
International students need to show they have the required level of English language to study this course. The required 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. |
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NB General Studies is not included in any offer.
Grades advertised on each programme webpage are the typical level at which our offers are made and provide information on any specific subjects an applicant will need to have studied in order to be considered for a place on the programme. However, if we receive a large number of applications for the programme we may not be able to make an offer to all those who are predicted to achieve/have achieved grades which are in line with our typical offer. For more information on how applications are assessed and when decisions are released, please see: After you apply
International Foundation programmes
Preparation for entry to Year 1 of an undergraduate degree:
Course content
The first year modules introduce you to the main areas of university-level Mathematics, with topics covered including Formal Mathematics and Proof, Analysis, Algebra, Calculus, Modelling, Probability and Statistics. Our Foundations of Mathematics module helps support background knowledge from A level, especially if you did not take Further Mathematics, and several modules help develop computer skills.
In your second year you can choose from a number of streams that develop your understanding of Real and Complex Analysis, Abstract and Linear Algebra, Applied Mathematical Modelling and Statistics. Optional modules give you the opportunity to learn about more specialised topics. Up to 30 credits of elective (free choice) modules can be taken, with at most 15 credits outside the disciplines of Engineering, Mathematics and Physics.
As you move in to the third year you can choose from many advanced topics directly related to fluid dynamics. You’ll also have the ability to choose from a wide variety of optional modules in advanced mathematics from across the department. Up to 30 credits of elective (free choice) modules can be taken from any discipline in the University subject to approval, pre-requisites, timetabling and availability.
In your fourth year you’ll take advanced mathematics modules covering various topics including Mathematical Modelling in Biology and Medicine. You’ll also undertake your MSci Project, this includes a substantial research element that will give you the chance to apply the mathematical and computational skills you have developed throughout your degree. There will be a range of potential projects to choose from to suit your interests and optional modules can be chosen to support your project. Up to 30 credits of elective (free choice) modules can be taken from any discipline in the University subject to approval, pre-requisites, timetabling and availability.
You may notice changes to some of our modules over the coming months. This is because we are making space for the following:
- Minors: Future Skills Pathways - Alongside your main degree you may be eligible (depending on your course) to choose modules from another subject to broaden your skills and interests.
- Skills to Thrive built into every degree - Essential skills for your future, including communication, problem-solving, teamwork and digital confidence.
- Increased innovation and wellbeing - More room for creative learning, real-world projects and a healthier study rhythm.
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.
120 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Foundations | 0 | |
| Mathematical Structures | 30 | |
| Mathematical Methods | 30 | |
| Mathematical Modelling | 30 | |
| Probability, Statistics and Data | 30 | |
MTH1000: Foundations
University level mathematics differs from that taught in schools not only in the difficulty of the topics and higher abstraction, but also in the style of teaching. This module aims to ease the transition to university level mathematics by bridging the gap between mathematics taught prior to university level, and the material covered in the first year of our mathematics degree, including the programming languages which will be taught in depth in other modules. The module eases you into a university teaching and learning environment and helps revise material from A-level. You will revisit essential skills in algebra, coordinate geometry, vectors, series and sequences, as well as some topics which are covered in Further Mathematics A-level such as complex numbers, matrix algebra, differential equations, and Maclaurin series. In this module, you will go over the theory and see many solved examples, as well as practice many examples to master these essential topics. Attending the lectures of this module is highly recommended to those students who do not have an A-level in Further Mathematics or equivalent, but those who do can also utilise these sessions to review the material and gain more practise experience. This module will also provide the skills needed to communicate mathematics which is a vital skill in all modules to be taken throughout a mathematics course.
MTH1001: Mathematical Structures
A key aspect of mathematics is its ability to unify and generalise disparate situations exhibiting similar properties by developing the concepts and language to describe the common features abstractly and reason about them rigorously. In this module, you will be introduced to the language of logic, sets, and functions which underpins of all modern pure mathematics, and will learn how to use it to construct clear and logically correct mathematical proofs. The content goes beyond mathematics taught at A-level: you will learn and use methods to prove rigorous general results about the convergence of sequences and series, justifying the techniques developed in MTH1002 and laying the foundations for a deeper study of Analysis in MTH2008. You will also learn the definitions and properties of abstract algebraic structures such as groups and vector spaces. These ideas are developed further in MTH2010 and MTH2011. The material in this module is fundamental to many other modules in the mathematics degree programmes. It underpins the topics you will see in more advanced modules in fundemental mathematics and enables a deeper understanding and rigorous justification of the mathematical tools you will meet in more applied mathematics modules and which are widely used in physics, economics, and many other disciplines.
MTH1002: Mathematical Methods
During your mathematics degree, you will be solving problems and proving theories in several branches of mathematics such as in pure mathematics, in applications to science and engineering, and in statistics. Inevitably you need to be able to calculate. That is what gives the mathematics its great power. This module covers developed bodies of useful techniques as a toolkit of common knowledge. It brings emphasis on the techniques rather than the applications of the techniques. Such techniques will enable you to deepen your familiarity with, and generalise, methods that you have seen at school level mathematics. This module will study topics that include the geometry of conic sections, properties of functions such as continuity and differentiability, differential and integral calculus, limits and convergence of sequences and series including Power Series and Taylor Series. The module also develops the fundamentals of vector and matrix theory, multivariate calculus, and the classification of various types of differential equations as well as analytical methods for solving them. The material in this module provide intuition for, and examples of, many of the mathematical structures that will be discussed in the module MTH1001 Mathematical Structures, and supply a firm understanding of methods required in future modules in the mathematics degree. In particular, it develops methods that underpin the modules MTH2003 Differential Equations and MTH2004 Vector Calculus and Applications.
MTH1003: Mathematical Modelling
This module will introduce you to the theory and tools for analysing real physical systems, such as pendulums, planetary motion, and predator-prey models. You will also develop programming and coding skills using a language such as Python, and learn how mathematical theory and computer-based modelling can complement each other to help us understand and predict the world around us.
This module will also introduce you to the process of mathematical research and help you to understand the nature of the mathematical research community that you will be joining at the University of Exeter. You will work individually or as part of a team to carry out three short projects that will develop a range of individual and group research and communication skills. The ideas and skills in the module are developed further in MTH2005 Modelling: Theory and Practise.
The module aims to introduce you to Newtonian dynamics and its applications; to show you the use of calculus and vectors in the modelling of physical systems; to introduce you to applied mathematics as a tool for investigating natural phenomena. As examples, you will explore the consequences of physical laws, as well as the behaviour of physical and natural systems from projectiles to predator-prey systems and planetary motion.
MTH1004: Probability, Statistics and Data
Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the mathematics of combinatorics and probability, and the key ideas of statistical modelling and inference, in which probability is used to quantify uncertainty. You will also gain experience of employing these ideas to analyse data using statistical software such as the R programming environment. The module develops key ideas and techniques that form the foundation of modules such as MTH2006 Statistical Modelling and Inference.
The aim of this module is to introduce you to basic topics in probability, statistics and data analysis. This module provides the foundation for the second-year stream in Statistical Modelling and Inference, and subsequent modules in statistics in years 3 and 4.
Please note that the module information displayed here is subject to change.
30 credits of compulsory modules, 90 credits of optional modules
You must select 30-60 credits from Optional Group 1
You must select 30-60 credits from Optional Group 2
Standard progression to Stage 3 of the MSci: Candidates must normally have achieved an average mark of at least 55% across the full 120 credits of assessment, including any failed and condoned modules. Students who do not reach the threshold may progress to Stage 3 of the equivalent BSc programme, as long as the standard progression requirements are met.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Differential Equations | 15 | |
| Vector Calculus and Applications | 15 | |
MTH2003: Differential Equations
Differential equations are at the heart of nearly all modern applications of mathematics to natural and man-made phenomena. Mathematically, all rates of change and acceleration can be described by derivative functions. These include the growth of populations, the spread of diseases, movement of physical objects in response to forces acting on them, or even the fluctuations of the stock market. This course will enable you to demonstrate an understanding of, and competence in, a range of analytical tools for posing and solving differential equations, and their application to situations in science and technology.
MTH2004: Vector Calculus and Applications
This module introduces vector calculus and its applications in particular fluid dynamics and electromagnetism. The module consists of two parts, which are closely linked. In the first part of the module, you will learn about the mathematical theory and techniques of vector calculus. You will develop your competence in using vector calculus in both differential and integral forms. The second part of the module gives an introduction to fluid dynamics and electromagnetism as two applications of vector calculus. It lays down some basic principles using a number of simplifying assumptions.
This introductory vector calculus course aims to increase your understanding of fluid dynamics and electromagnetism. It examines how one can use vector formalism and calculus together to describe and solve many problems in two and three dimensions. For example, the rules that govern the flow of fluids can be described using vector calculus, with resulting laws of motion described by partial differential equations rather than ordinary differential equations.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Real Analysis | 15 | |
| Complex Analysis | 15 | |
| Groups, Rings and Fields | 15 | |
| Linear Algebra | 15 | |
| Optional 2 | ||
| Statistical Modelling and Inference | 30 | |
| Numerical Modelling | 15 | |
| Mathematics of Machine Learning and AI | 15 | |
MTH2008: Real Analysis
Description – summary of the module content
Infinite processes appear naturally in many contexts, from science and engineering to economics. From solving the equation that finds the wave function of a quantum system in physics, processing sensor data in engineering, to calculating prices for options in economics, at the foundation of all of these are infinite processes and the pure mathematics developed to rigorously and correctly handle these processes. That field of pure mathematics is called analysis, and the central object of study in analysis is that if a limit, which further extends to the notions of convergence, continuity, differentiation and integrability.
In this module, you will be introduced to the pioneering work of Cauchy, Riemann and many other notable mathematicians. By building on material from MTH1001 and MTH1002, we will carefully and rigorously develop how to handle real-variable differentiation, Riemann integration, power series, and basic notions of point set topology.
The material in this module is a prerequisite for the study of Complex Analysis (MTH2009) Topology and Metric Spaces (MTH3040) and Fractal Geometry (MTHM004). It is also recommended for those studying Dynamical Systems and Chaos (MTHM018), and is the basis for applications in economics, engineering and physics.
Pre-requisite modules
MTH1001 and MTH1002 (or equivalent)
Aims – intentions of the module
MTH2009: Complex Analysis
The central object of study in analysis is the notion of a limit and related concepts of convergence, continuity, differentiation, and integration.
The objective of this module is to provide you with a rigorous introduction to complex analysis. We will carefully develop an understanding of the analysis of functions of a complex variable, and prove the central theorems governing the differentiation and integration of such functions. You will learn how to handle power series, singularities and contour integration, and see how to apply these to solve a wide range of problems. Quite surprisingly, complex analysis turns out to be a great deal more rigid, and more algebraic, than real analysis, and has many practical applications.
The material in this module has close links with Vector Calculus MTH2004 (although these modules are logically independent), and provides the foundations for further study in a range of subjects, most notably in number theory and geometry.
MTH2010: Groups, Rings and Fields
In this module, you will explore some of the key techniques of modern algebra, including groups, rings, and fields. These topics have their roots in the desire to solve certain equations that arise from arithmetic and geometry.
The most familiar example of a ring is the set of all integers Z=...,-3,-2,-1,0,1,2,3... equipped with the usual operations of addition and multiplication. The familiar properties of these operations serve as a model for the axioms for rings. We can consider whether certain equations have solutions in rings such as the integers. For example, Fermat's Last Theorem famously asserts that if n is a fixed integer that is at least 3, then the equation x^n + y^n = z^n has no solutions for which x, y and z are non-zero integers. Though this problem is easy to state, its solution is extremely difficult: it was first stated in 1637 but the first complete and correct proof was given in 1994. Ring theory is essential for the fourth year module MTHM028 Algebraic Number Theory, which in turn lays the foundations for solving problems such as Fermat's Last Theorem.
Fields are special types of ring in which every non-zero element has a multiplicative inverse. Examples include the rational numbers Q, the real numbers R and the complex numbers C.
MTH2011: Linear Algebra
Abstract vector spaces are important objects in linear algebra, which has its origins in solving linear equations over a field such as the rational, real or complex numbers. The elements of a vector space can be somewhat abstract: for example, they can be functions. However, it is precisely this abstraction that makes the theory of vector spaces such a powerful tool. They arise in almost every area of (pure and applied) mathematics and statistics. For example, PDEs (partial differential equations) of some types are just ODEs (ordinary differential equations) in vector spaces of functions, and numerical and data analysis methods consider vector spaces of increasing dimension to approximate function spaces.
Prerequisite modules: MTH1001and MTH1002 (or equivalent).
This module aims to develop the theories and techniques of modern algebra, particularly in relation to vector spaces and inner product spaces.
MTH2006: Statistical Modelling and Inference
Statistical modelling lies at the heart of modern data analysis, helping us to describe and predict the real world. Statistical inference is the way that we use data and other information to learn about and apply statistical models. In this module, you will learn the theory underpinning modern statistical methods such as fitting normal linear models, evaluating how well they fit the data and taking inferences from it. You will apply the theory using statistical software such as R to analyse and draw conclusions from a range of real-world data sets. Topics covered in the module range from estimators, confidence intervals, design of experiments and hypothesis testing to statistical modelling, regression, inference and comparison of models. Skills developed in the module are taken further in modules such as MTH3012 Advanced Statistical Modelling.
This module aims to develop understanding and competence in statistical modelling by introducing you to the Normal linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.
MTH2014: Numerical Modelling
Mathematicians are problem solvers – we take a problem and choose the appropriate tool to solve it. Numerical methods are one of our most powerful tools, especially when using a computer. However, one problem is that computers will often give us an answer, but is it the correct answer? This module will build on MTH1003 to explore advanced numerical methods, exploring when they do and do not work. You will have lectures and practical sessions where real-world applications are explored using Python. The module will prepare you for real-world uses of numerical mathematics and prepare you for future computational modules. The module’s main aim is to equip you with an array of tools to solve real-world problems numerically, but also the mathematical ability to predict and analyse whether these methods will work and when they might fail. One other focus of the module will be efficiency – there are often many ways to numerically solve a problem, but we want to understand which method is the mMTH2015: Mathematics of Machine Learning and AI
This module introduces mathematical foundations of modern machine learning (ML) and artificial intelligence (AI). It covers the mathematical theory of learning (PAC learning), analysis of machine learning algorithms (eg decision trees, artificial neural networks) as mathematical methods for function approximation, and gradient-based optimisation as a paradigm for training ML models for specific tasks. Practical work includes studying code examples of machine learning applications in different fields, and guided projects on advanced topics in ML and AI, such as Natural Language Processing, Formal Proof Systems, and Search Algorithms. Programming/Coding: The main programming language for the examples in this module is python. Students will receive guidance on how to translate examples from python to R. The emphasis of the course is to gain understanding of mathematical foundations of ML and AI and practical experience on worked examples and real-world applications. The module suits studePlease note that the module information displayed here is subject to change.
45 credits of compulsory modules, 75 credits of optional modules
You must select 45-75 credits from Optional Group 1
You may select 0-30 credits from Optional Group 2
You may select up to 30 credits of free choice modules at NQF Level 5 (Stage 2) or NQF Level 6 (Stage 3)
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Mathematical Biology and Ecology | 15 | |
| Computational Nonlinear Dynamics | 15 | |
| Research in Mathematical Sciences | 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
MTH3039: Computational Nonlinear Dynamics
Nonlinear dynamical systems are used in almost all disciplines: from applied mathematics to physics of any kind, to biology, chemistry, sociology, ecology, economics, engineering, and computer science. This is also why this module is welcoming students from all disciplines, from mathematics to sociology. The nonlinearity of systems makes them widespread, but it comes at a price: almost nothing about nonlinear systems can be estimated analytically.
Computational nonlinear dynamics is the process of studying nonlinear dynamical systems by devising and running numerical algorithms. Throughout this module we will be discussing many interesting aspects of nonlinear dynamical systems, such as multistability, deterministic chaos, critical transitions, … (see Topics Covered). For each aspect, we will be devising algorithms that can identify it for arbitrary dynamical systems. In the coursework we will be creating computer programs that apply these algorithms to dynamical systems. Sometimes we may have data obtained directly from some real-world source instead of a dynamical system, but the process will be the same. As such, this module will not only teach you how nonlinear dynamical systems behave, and how to understand them, but also how to design computer algorithms that fulfil a certain goal. This is an invaluable experience for your future employability in a world increasingly reliant on programming.
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.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Theory of Weather and Climate | 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 | |
| Topology and Metric Spaces | 15 | |
| Bayesian Statistics, Philosophy and Practice | 15 | |
| Integral Equations | 15 | |
| Statistical Computing | 15 | |
| Dynamical Systems and Chaos | 15 | |
| Statistical Data Modelling | 15 | |
| Optional 2 | ||
| Commercial and Industrial Experience | 15 | |
| Aerosols, Clouds and Climate | 15 | |
EMP3001: Commercial and Industrial Experience
This module will provide you with an opportunity to undertake practical work experience in a business, commercial or public sector setting that is of direct relevance to your development as an experienced professional. You will apply the knowledge and skills from taught modules to authentic problem solving in the workplace, which will give you important insights into your potential job role once you graduate from university. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills. Placements will involve taking responsibility for a substantial project, which may be a problem to be solved in the host organisation, in line with your degree programme. Placements are subject to availability, approval by the module convener and full compliance with important Health and Safety procedures and requirements. Placements are normally three months some time during May-September, finishing before autumn classes start. Placements must be a minimum of six weeks full time. It is understood that this will entail around 210 hours of supervised work in order to generate the depth of experience equivalent to the 125 hours of self‑directed study on a focused topic specified under the regulations, as workplace activity is not counted directly as academic study International placements are allowed. Placements can be paid or volunteer.
NSC3009: Aerosols, Clouds and Climate
Climate change is arguably one of the most urgent issues over the next two decades as humanity struggles to meet the 1.5C above pre-industrial target set by the Paris COP21. Concentrations of both greenhouse gases (GHG) and aerosols (particulate matter suspended in the atmosphere) have increased considerably since pre-industrial time. Whilst anthropogenic emissions of GHG warm the planet, aerosol emissions exert a significant, yet poorly quantified cooling that acts to offset a fraction of global warming from GHG.
Reducing current uncertainties associated with estimates of climate change sensitivity to GHG emissions is hampered by our understanding of the strength of the cooling effect aerosol particles have on the climate via their interactions with clouds and sunlight. Despite decades of research the Intergovernmental Panel on Climate Change Assessment Report continue to highlight our low understanding of aerosol-cloud-interactions (ACI) as the key uncertainty hampering our understanding of climate change.
This module is designed to explore the atmospheric physical processes determining the role of aerosols and their interaction with clouds on the climate to provide insight on the importance in reducing current uncertainties associated with aerosol - cloud - interactions (ACI) for adoption of more robust adaptation and mitigation strategies.
Please note that the module information displayed here is subject to change.
60 credits of compulsory modules, 60 credits of optional modules
You must select 30-60 credits from Optional Module Group 1
You may select up to 30 credits of free choice modules at NQF Level 6 (Stage 3) or NQF Level 7 (Stage 4) which may include BIOM*** modules. You must take at least 120 credits at NQF Level 7 in Stages 3 and 4 combined
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| MSci Project | 45 | |
| Mathematical Modelling in Biology and Medicine | 15 | |
MTHM040: MSci Project
In this module, you will write an independent project with a substantial research element that will give you the chance to apply the mathematical and computational skills you have developed throughout your degree. Using your independent learning skills, you will undertake the project individually, under the supervision of an expert from the discipline specific to your MSci programme. There will be a range of potential projects to choose from, spanning the spectrum from theoretical problems to others more focused on the development of specialist computer programmes and packages.
The aim of the module is put into practice the knowledge you have acquired so far in your degree programme, and to engage you with modern scientific developments in a specialist field of study. You will gain experience of many aspects of research work. These will include: literature review; planning; experimentation and analysis; interpretation of results; technical report writing; and oral presentation. You will be able to exercise more autonomy on this research project than in more formal modules.
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 | |
| Metric Number Theory and Diophantine Approximation | 15 | |
| Dynamical Systems and Chaos | 15 | |
| Fluid Dynamics of Atmospheres and Oceans | 15 | |
| Modelling the Weather and Climate | 15 | |
| Algebraic Number Theory | 15 | |
| Algebraic Curves | 15 | |
| Waves, Instabilities and Turbulence | 15 | |
| Magnetic Fields and Fluid Flows | 15 | |
| Statistical Modelling in Space and Time | 15 | |
| Space Weather and Plasmas | 15 | |
| Ergodic Theory | 15 | |
| Mid-latitude Weather Systems | 15 | |
| Data-driven Analysis and Modelling of Dynamical Systems | 15 | |
| Uncertainty Quantification | 15 | |
| Statistical Data Modelling | 15 | |
Fees
Tuition fees for 2026 entry
UK students: £9,790 per year
International students: £30,100 per year
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 scholarships for sport, music and other achievements, alongside regional and partner awards such as Chevening, The Beacon Trust and the British Council. Financial support is available for students from disadvantaged backgrounds, lower income households and other under-represented groups to help them access, succeed and progress through higher education.
* Terms and conditions, including deadlines, apply. See our website for details.
Learning and teaching
All our degrees involve a combination of teaching methods, including lectures, seminars, examples classes, workshops and tutorials. Most modules in mathematics involve three one-hour lectures per week, so you typically have 12 lectures per week. In the first year there are tutorial classes for each module every fortnight, except for modules involving computing or project work. Thus in the first year you would typically have around 16 contact hours per week. In the first term, the ‘Foundations’ module helps you with the transition from A level to university mathematics.
Private study and support
In addition to lectures and seminars, you should spend about 20 hours per week in private study. Working through examples and solving problems is a vital part of learning mathematics, and we advise you attempt all coursework problems, whether formally assessed or not. You will be allocated a personal tutor who will be happy to advise or put you in touch with support services and you are encouraged to discuss mathematical problems or questions with tutors and lecturers who advertise regular office hours. Extra support is available, for example through lunchtime mathematics surgeries or our peer mentor scheme, and we have an active student-staff liaison committee.
Project and computer work
There are modules at all levels that involve project work and report writing, and the final year project is a major piece of research and writing that allows you to go into depth for a specific area under the guidance of a member of academic staff. You can choose from wide range of possible project topics each year, or negotiate a topic/title with a member of academic staff. Several of the modules develop skills to use a range of modern computer tools for working with data, programming or symbolic algebra as well as typesetting and presentation.
Elective modules
Once you have mastered the foundations, our mathematics programmes offer in later years a wide range of options within the programme. In addition to the named degrees with study abroad, professional experience and year in industry, you can take optional (called elective) modules from all over the university in later years. These options are subject to your availability, having the appropriate background (pre-requisites) and certain programme constraints.
A research and practice led culture
You will benefit from teaching by academic staff comprising internationally-recognised mathematicians, scientists and practitioners active across a wide range of topics in pure and applied mathematics, statistics and applications. As you progress through your degree, you will hear about the latest mathematical research and have opportunities (for example, the independent research project) to become actively involved in a research project yourself.
Assessment
Assessment for all degrees is through a combination of examinations and coursework. Examinations are the more important part of the process, but the coursework helps you to work steadily throughout your degree. This is particularly important in Mathematics where the subject matter develops logically as the degree progresses. Written examinations for mathematics modules are held in January and May/June of the first and second years and in May/June of each subsequent year. Some modules have tests, essays, presentations and/or project reports that contribute to the assessment.
Optional modules outside of this course
Each year, if you have optional modules available, you can take up to 30 credits in a subject outside of your course. This can increase your employability and widen your intellectual horizons.
Minors: Future Skills Pathways
You can study a Future Skills Pathway alongside your main degree by choosing up to 30 credits of modules from a different subject area in your second and final years.
Your future
Mathematics makes vital contributions to biological research, using sophisticated techniques to extract useful information from genetic data. With the completion of the Human Genome Programme, there is huge potential for advances in research by exploiting the newly-available data. As more and more high-tech companies are set up to exploit applications of this bioinformatics revolution, there are likely to be many career opportunities for graduates of this programme in commercial and academic environments.
Exeter has an excellent reputation with graduate recruiters and a strong employment record. Our graduates excel in specialist mathematical fields and across a broad range of other sectors. We offer a very wide range of opportunities for you to develop the skills employers are looking for.
You’ll be able to meet with local and national employers who regularly visit the university to engage with students, hosting mock interviews, CV workshops, drop-ins and lectures. This is a great opportunity for you to find out more about the day to day activities of their business and recruitment opportunities. Our Careers Service also host a wealth of employer activity, such as Careers Fairs, so you’ll never be short of chances to network with potential employers.
Professional experience
You have the choice to take an optional ‘Commercial and Industrial Experience’ module during the vacation before your third year. This opportunity allows you to gain paid work experience in a commercial setting while earning credits towards your degree programme. Professional experience not only develops your CV but helps you to determine your career aspirations.
Career paths
The broad-based skills acquired during your degree will give you an excellent grounding for a wide variety of careers, not only those related to Mathematics but also in wider fields. Examples of roles recent graduates are now working as include:
- Accountant
- Actuary
- Analyst Programmer
- Business Analyst
- Credit Risk Analyst
- Data Science Developer
- Investment Analyst
- Software Engineer
- Statistician
- Tax Manager
