We welcome applications from the United States of America
We've put together information and resources to guide your application journey as a student from the United States of America.
Overview
Top reasons to study with us
10
10th for Mathematics
The Guardian University Guide (2025)
11
11th for Mathematics
The Complete University Guide (2026)
100% of our research impact rated outstanding (REF2021)
Mathematics is an incredibly powerful subject that sits at the foundation of all science and technology. Our world is technologically advancing at a rapid pace thanks to the application of mathematics in areas such as cyber security, health, environmental science, and engineering. Graduate talent is desperately needed to drive these innovations which will soon be commonplace in all aspects of our lives. So if you think a maths degree only leads to careers in finance, accounting or business management, think again. With a degree in Mathematics, Artificial Intelligence and Real-world Systems (MARS), you will have the tools to forge a successful and rewarding career in technologies that will dictate our futures.
Broaden your horizons
Enrich your university experience with a year overseas at one of our partner universities. In Year 3, head out to start your adventure and immerse yourself in a different cultural and academic community. We’ll support you all the way!
What to expect
Our four-year BSc Hons Mathematics, Artificial Intelligence and Real-world Systems (MARS) (Study Abroad) degree begins by building upon your understanding of mathematical methods and concepts through a mix of lectures and workshops. You will explore a wide range of topics, from differential equations, vector calculus, probability and statistics to logic, proofs and theorems. In addition to this, computing skills are essential, which is why you will learn the principles of scientific computing and gain experience working with the R and Python programming languages.
As you progress into Years 2 and beyond, you will delve deeper into a range of specialist optional modules alongside covering key concepts in linear algebra, and probability. You will learn to translate contemporary issues into mathematical problems and develop the knowledge and techniques needed to create solutions. Tools such as multivariate calculus and mathematical analysis will be used to understand the algorithms that have revolutionised machine learning and artificial intelligence. This will enable you to explore machine learning, AI, and statistical methods, with the ability to investigate more advanced mathematical models and their solutions. As part of this, you will undertake substantial industry-inspired projects, working as an individual and as part of a small group.
Personal development
You will develop valuable transferrable skills such as data analysis, problem-solving and quantitative reasoning, all of which make you highly desirable to future employers. These skills are honed by working in collaboration with fellow students, ruminating on theories and testing them out, delivering presentations and communicating your research results.
We hope you find your year overseas personally enriching. Our students often tell us that they return feeling more confident, self-assured and with a broader perspective to take into job interviews.
A supportive community
To help you transition from A-level to degree-level study, the School of Mathematical Sciences hosts weekly workshops, problem-solving classes, and one-to-one sessions. If you wish to engage with mathematics beyond that, the MathSoc hosts a weekly Maths Café that includes access to academic support and a casual space to chat with other students.
3 things our students want you to know:
Lancaster has received £13million from Research England to become a leader in the mathematics underpinning AI, meaning you’ll be part of a community delivering solutions to problems in health, engineering, cyber security and the environment. This means that what you learn is always evolving and keeping with the pace of innovation
Our computing and maths societies put on industry talks, guest lectures and career development opportunities
Mathematical sciences at Lancaster are incredibly collaborative. You will bounce ideas around with experts, or with students from all years. Our thriving postgraduate research student community has been right where we are, asking the same questions, and there’s even opportunities to talk with them and learn from them
As a graduate of Lancaster, you will enjoy excellent employment prospects. Your qualification in Mathematics, Artificial Intelligence and Real-world Systems (MARS), along with your problem-solving skills, analytical abilities, and AI expertise, will make you highly desirable to employers in almost every industry and sector. This includes technology and AI, finance and insurance, engineering, environmental and climate science, healthcare and biomedical research, and government. The types of roles you may progress into include data scientist, data analyst, risk analyst, climate modeller, software engineer, scientific programmer, actuary, machine learning engineer, and mathematical engineer. Many of these roles are available across multiple sectors allowing you to choose a pathway that reflects your interests.
This degree will equip you to work in a variety of global businesses in a range of fields, including, but not limited to:
Software development and software engineering (Google, Apple, Microsoft)
Robotics and automation (ABB Robotics, DENSO, KUKA)
Cyber security (GCHQ, NCSC, Cisco)
Climate modelling and analytics (Met Office, CEDA, Climate X)
Bioinformatics and computational biology (The Francis Crick Institute, The Pirbright Institute, Wellcome Sanger Institute)
Health data science (NHS, UKHSA, HDRUK)
Medical imaging (Seimens, Philips, Canon)
Investment banking (Barclays, Morgan Stanley, Goldman Sachs)
Your transferable skills will also allow you to expand into roles in marketing, management, and consultancy.
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with the relevant life and work-based skills. We are unique in that every student is eligible to participate in The Lancaster Award which offers you the opportunity to complete key activities such as work experience, employability awareness, career development, campus community and social development. Visit our employability section for full details.
Skills for your future
A degree in mathematics will provide you with both a specialist and transferable skill set sought after by employers across a wide range of sectors.
Careers support
We are committed to developing your employability skills. Our dedicated Careers Officer works in partnership with the University’s Careers Service to offer a range of workshops and talks. You can also access 1:1 appointments throughout the year through the University’s Careers Service.
Placement year
Choosing a Placement or Industry pathway degree involves spending the third year of your four-year degree working full-time in a business. Many students find that a placement year helps them to decide which career path they would like to take. The experience will give you a strong advantage when looking for employment after your degree.
Internship scheme
Undertaking relevant work experience while you are at university helps you to apply for graduate-level jobs. Through our Internship Scheme, you can apply for paid work placements. These give you the opportunity to practice the skills and knowledge learned during your degree. These opportunities can be both full and part-time, and range from 3 months to a year.
Our alumni stories
Listen to our Mathematical Sciences alumni as they tell us how studying at Lancaster helped to prepare them for their future careers within mathematics.
Entry requirements
These are the typical grades that you will need to study this course. This section will tell you whether you need qualifications in specific subjects, what our English language requirements are, and if there are any extra requirements such as attending an interview or submitting a portfolio.
Qualifications and typical requirements accordion
A*AA. This should include Mathematics grade A or Further Mathematics grade A. The overall offer grades will be lowered to AAA for applicants who achieve both Mathematics and Further Mathematics at grades AA.
Considered on a case-by-case basis. Our typical entry requirement would be 45 Level 3 credits at Distinction, but you would need to have evidence that you had the equivalent of A level Mathematics grade A.
We accept the Advanced Skills Baccalaureate Wales in place of one A level, or equivalent qualification, as long as any subject requirements are met.
D*DD considered alongside both A level Mathematics grade A on a case-by-case basis
A level Mathematics grade A* plus A level grade A in a second subject and BTEC D, or plus BTEC DD on a case-by-case basis
38 points overall with 17 points from the best 3 HL subjects including 6 in Mathematics HL (either analysis and approaches or applications and interpretations)
We are happy to admit applicants on the basis of five Highers, but where we require a specific subject at A level, we will typically require an Advanced Higher in that subject. If you do not meet the grade requirement through Highers alone, we will consider a combination of Highers and Advanced Highers in separate subjects. Please contact the Admissions team for more information.
Only considered alongside A level Mathematics grade A
Help from our Admissions team
If you are thinking of applying to Lancaster and you would like to ask us a question, complete our enquiry form and one of the team will get back to you.
Delivered in partnership with INTO Lancaster University, our one-year tailored foundation pathways are designed to improve your subject knowledge and English language skills to the level required by a range of Lancaster University degrees. Visit the INTO Lancaster University website for more details and a list of eligible degrees you can progress onto.
Contextual admissions
Contextual admissions could help you gain a place at university if you have faced additional challenges during your education which might have impacted your results. Visit our contextual admissions page to find out about how this works and whether you could be eligible.
Course structure
Lancaster University offers a range of programmes, some of which follow a structured study programme, and some which offer the chance for you to devise a more flexible programme to complement your main specialism.
Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes and new research. Not all optional modules are available every year.
At university, emphasis is placed on understanding general mathematical theorems. they apply in many different cases, and understanding why a result is true enables us to creatively use the underlying ideas to tackle new problems.
Study the language and structure of mathematical proofs, illustrated by results from number theory. You will see the concept of congruence of integers, which is a simplified form of arithmetic where seemingly impossible problems become solvable. In relation, you’ll encounter the abstract idea of an equivalence relation.
Sets and functions form the basic language of mathematics. You will study functions of a real variable and abstract functions between arbitrary sets, and you will explore how to count sets, both finite combinatorial arrangements and infinite sets.
You will survey the language of networks, studying relations and how to model real-world events. Throughout the module you will practise writing concise and rigorous mathematical arguments.
A mathematical model is a representation of a real-world event, such as a building vibrating during an earthquake or the spread of a disease within a population. In this module, you will investigate mathematical models that lead to ordinary differential equations and will study a variety of core analytical methods for solving them, such as integrating factors and separation of variables.
You will learn to develop models by extracting important data from real-world scenarios, which can then be analysed and refined. Many mathematical models, including those used in artificial intelligence, are unmanageable, and thus you will establish and practice fundamental programming skills and concepts that will be used in future modules.
Interested in how mathematicians build theories from basic concepts to complex ideas, like eigenvalues and integration? Journey from polynomial operations to matrices and calculus through this module.
Starting with polynomials and mathematical induction, you will learn fundamental proof techniques. You will explore matrices, arrays of numbers encoding simultaneous linear equations, and their geometric transformations, which are essential in linear algebra. Eigenvalues and eigenvectors, which characterise these transformations, will be introduced, highlighting their role in applications including population growth and Google's page rankings.
Next, we will reintroduce you to calculus, from its invention by Newton and Leibniz, to its formalisation by Cauchy and Weierstrass. You will explore sequence convergence, techniques for evaluating limits, and key continuity tools like the intermediate value theorem. Differentiation techniques develop a geometric understanding of function graphs, leading to mastering integration methods for solving differential equations and calculating areas under curves. We conclude with a first look at vector calculus.
Many real-world problems seek to understand the function of a vector, where the vector could be a position in space, a direction, or the weights of a neural network. In this module, you will explore the world of multivariate techniques and multivariate calculus, deepening your understanding of vectors, angles, curves, surfaces and volumes, dimensional space, and alternative co-ordinate systems. You will encounter multidimensional derivatives, integrals and stationary points, and practice multidimensional analogues of techniques such as the chain rule and integration by substitution.
You will work with mathematical models that draw upon real-world problems, increasing in complexity throughout.
An introduction to the mathematical and computational toolsets for modelling the randomness of the world. You will learn about probability, the language used to describe random fluctuations, and statistical techniques. This will include exploring how computing tools can be used to solve challenges in scientific research, artificial intelligence, machine learning and data science.
You will develop the axiomatic theory of probability and discover the theory and uses of random variables, and how theory matches intuitions about the real-world. You will then dive into statistical inference, learning to select appropriate probability models to describe discrete and continuous data sets.
You will gain the ability to implement statistical techniques to draw clear, informative conclusions. Throughout, you will learn the basics of R or Python, and their use within probability and statistics. This will equip you with the skills to deploy statistical methods on real scientific and economic data.
Symmetry is central to our understanding of a range of subjects, from the structure of molecules to the roots of polynomials. In this module, you will see how group theory naturally appears whenever we look at symmetry.
Using familiar examples, including the symmetry of regular polygons, rotations and reflection matrices, roots of 1 in the complex plane, and permutations, you will define what makes a group and how this can provide a unifying language, highlighting connections between seemingly different subjects.
You will then transition into mathematical analysis, developing an approach to sequences, limits, and continuity that provides the foundation for calculus. Examining a range of examples, you will build your understanding of precise mathematical reasoning and gain an appreciation for the importance of proof, generalisation and abstraction.
Throughout the module, you will develop the ability to approach problems in both an analytical and creative way, preparing you for more advanced study.
Core
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Never has the collection of data been more widespread than it is now. The extraction of information from massive, often complex and messy, datasets brings many challenges to fields such as statistics, mathematics and computing.
Develop the skills and understanding to apply modern statistical and data-science tools to gain insight from contemporary data sets. By addressing challenges from a variety of applications, such as social science, public health, industry and environmental science, you will learn how to perform and present an exploratory data analysis, deploy statistical approaches to analyse data and draw conclusions, as well as developing judgement to critically evaluate the appropriateness of chosen methods for real-world challenges.
Building on your knowledge of vectors and matrices, this module explores the elegant framework of linear algebra, a powerful mathematical toolkit with remarkably diverse applications across statistical analysis, advanced algebra, graph theory, and machine learning.
You'll develop a comprehensive understanding of fundamental concepts, including vector spaces and subspaces, linear maps, linear independence, orthogonality, and the spectral decomposition theorem.
Through individual exploration, small-group collaboration, and computational exercises, you'll gain both theoretical insight and practical skills. The module emphasises how these abstract concepts translate into powerful problem-solving techniques across multiple disciplines, preparing you for advanced studies while developing your analytical reasoning abilities.
Machine learning is at the heart of modern AI systems, and it is a fundamentally mathematical subject. You will learn this mathematics by discovering how techniques are deployed in several AI systems, including the neural networks that have revolutionised the field.
You’ll start by building connections with previously encountered approaches through the unifying concept of a loss function of a parameter vector. For example, with a neural network model the vector input is the set of weights, and the loss function might be the prediction error on a dataset.
The goal is to find a vector input that produces a small loss; in the above example, this is known as training the neural net. You will learn and deploy some of the key mathematical ideas and numerical techniques, such as back propagation and stochastic gradient descent, that enable the automated iterative learning of a good vector input.
Many real-world problems give rise to integrals and ordinary differential equation (ODEs) that cannot be solved analytically. To start, you will be introduced to techniques for tackling such problems, beginning with fundamental numerical methods, such as the trapezium rule and Euler’s method, before progressing to more advanced techniques and quantifying the accuracy, stability and limitations of these methods. Alongside numerical approaches, you will also develop heuristic methods to characterise a system's limiting behaviour.
Many familiar phenomena, from the pulses of light down a fibre optic cable to the shudder of turbulence on a plane, involve multiple variables, such as time and position. Their mathematical description requires differentiation with respect to each of these independent variables, leading to partial differential equations (PDEs). You will learn how to formulate PDEs for complex, real-world problems and practice core techniques for solving them.
Statistics allows us to estimate trends and patterns in data and gives a principled way to quantify uncertainty in these estimates. The findings can lead to new insights and support decision-making in fields as diverse as cyber security, human behaviour, finance and economics, medicine, epidemiology, environmental sustainability and many more.
Dive into the behaviour of multivariate random variables and asymptotic probability theory, both of which are central to statistical inference. You will then be equipped to explore one of the most fundamental statistical models, the linear regression model, and learn how to apply general statistical inference techniques to multi-parameter statistical models. Statistical computing is embedded in the module, allowing you to investigate multivariate probability distributions, simulate random data, and implement statistical methods.
Continuing with your study into real numbers, you will explore their completeness (the idea that there are no ‘gaps’, unlike in the rationals). This completeness will be used to understand the limits of sequences, convergence of series, and power series.
This framework will allow for precision when exploring continuity, differentiability, and integrability of functions of a real variable, providing an improved foundation for calculus. That will enable you to understand when it is appropriate to use calculus; for instance, in proving theorems in other areas of mathematics, such as mathematical physics, probability and number theory.
The cornerstone of mathematical analysis is the construction of proofs involving arbitrarily small numbers, so-called epsilons and deltas. You will have opportunities to practise and improve your management of these quantities, in the process developing your skills in logic, communication and problem-solving.
Core
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Study at one of our approved international partner universities in your year abroad. This will help you to develop your global outlook, expand your professional network, and gain cultural and personal skills. It is also an opportunity to gain a different perspective on your major subject through studying the subject in another country.
You will choose specialist modules relating to your degree and also have the opportunity to study modules from other subjects offered by the host university.
Places at overseas partners vary each year and have previously included universities in Australia, USA, Canada, Europe, New Zealand and Asia.
Core
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Problem solving, gathering relevant information, writing, presenting and working as part of a group are essential in almost every professional career. You will develop and practice these skills as you undertake both individual and group-based, industry-linked and MARS-related projects.
You will also integrate prose, mathematics, computer code and its output into a clear, well-structured and appropriately linked Markdown document - a key skill in reproducibly documenting your research.
Optional
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Differential equations are fundamental to mathematical modelling, with multiple applications in engineering, biology, and the environment.
Explore both ordinary and partial differential equations (ODEs and PDEs), with an introduction to advanced solution techniques and real-world applications. You will understand the deeper theory of ODEs and how solutions lead to special functions through series expansion, including Bessel functions. You will be introduced to Fourier series as a foundational tool in modern science.
Shifting your focus to PDEs, you will classify second-order equations and solving key equations in diverse geometries, noting the importance of boundary conditions. Applications will include acoustics, pollution dispersal, groundwater flow and forms of medical imaging. By the end of the module, you will have mastered analytical methods for solving differential equations and gained the intuition to interpret their solutions in scientific and engineering contexts.
Models of dynamical systems are fundamental to our understanding of the physical and natural world.
Explore a new class of model for the time evolution of a dynamical system and investigate Markov jump process models for real-world systems, such as the evolution of species populations in the wild and the spread of infectious diseases. Using these processes, you will learn how to simulate and study methods understanding their properties and behaviours. Unlike deterministic differential equation models, Markov jump processes are random, allowing for different behaviour every time they are simulated. You will discover how it is often possible to associate a jump process with a related differential equation approximation and that this can provide important insights into the behaviour of the jump process and the original real-world system.
An introduction to a variety of methods that are useful for analysing environmental data, such as air temperatures, rainfall or wildfire locations. Spatial dependence is a key feature of many environmental datasets, and the Gaussian process will be introduced as a model for continuous spatial processes. You will learn about the properties of the Gaussian process and implement this model for spatial data analysis, before investigating methods for point-reference data, such as earthquake or wildfire locations.
You will also dip into natural hazard risk management, which seeks to mitigate the effects of events, such as flooding or storms, in a manner that is proportionate to the risk. You will learn basic concepts from extreme value theory, including the appropriate distributions for extremes, and how to use these as statistical models for estimating the probability of events more extreme than those in the dataset.
The study of graphs (mathematical objects used to model networks and pairwise relations between objects) is a cornerstone of discrete mathematics. Graphs can represent important real-world situations, and the study of algorithms for graph-theoretical problems has strong practical significance.
You will learn about structural and topological properties of graphs, including graph minors, planarity and colouring. We will introduce several theoretical tools, including matrices relating to graphs and the Tutte polynomial. We will also study fundamental algorithms for network exploration, routing and flows, with applications to the theory of connectivity and trees, considering implementation, proofs of correctness and efficiency of algorithms.
You will gain experience in following and constructing mathematical proofs, correctly and coherently using mathematical notation, and choosing and carrying out appropriate algorithms to solve problems. The module will enable you to develop an appreciation for a range of discrete mathematical techniques.
An inner product space is a real or complex vector space, equipped with certain extra structure that formalises the geometrical notion of orthogonality. It turns out that each inner product space has an intrinsic notion of distance, allowing us to discuss convergence and completeness. Complete inner product spaces are known as Hilbert spaces.
The theory of Hilbert spaces blends linear algebra and (real) analysis. It is a natural and powerful tool for studying problems of quantitative approximation. Furthermore, it provides an abstract framework that can be applied to diverse areas of maths, from differential equations and spectral theory to quantum mechanics and stochastic processes.
This module will introduce you to the theory of Hilbert spaces and prepare for advanced study in functional analysis, approximation theory, signal processing, and statistical learning.
Linear systems of differential and integral equations provide a mathematical model for a wide range of real-world devices, including communication systems, 5G networks, electrical circuits, heating systems and economic processes. Mathematical analysis of these models gives insight into the behaviour of these devices, with applications in automatic control, signal processing, wireless communications and numerous other areas.
Linear systems are considered in continuous time that reduce to a standard (A,B,C,D) state space representation. Via the Laplace transform, these are reduced further to rational transfer functions. Linear algebra enables us to classify and solve (A,B,C,D) models, while we describe their properties via diagrams in standard computer software. You will consider feedback control for linear systems, describing the rational controllers that stabilise an (A,B,C,D) system. Alongside the development of analytic methods to study linear systems, you will also gain experience in modelling real-world devices by such systems.
AI is suddenly everywhere and the methods for training and using AI tools are fundamentally mathematical, and fascinating in their own right. By understanding what goes on ‘under the hood’ you will open up a plethora of exciting opportunities for both further study and employment.
We will introduce you to the deep learning architectures used in modern AI. You will investigate how different architectures work with different data types and tasks, and what the computationally specified architectures actually mean in a modelling sense.
However, deep neural nets need more than just an appropriate architecture; they need to be both trained and deployed. You’ll study the interesting maths at each of these stages: the most recent approaches to loss function minimisation, and the techniques to sequentially learn and adapt to new data and observations, a critical component of modern AI methods.
A metric space consists of a set, whose elements are called points, and a notion of distance between points governed by three simple rules, abstracted from basic properties of Pythagorean distance in the Euclidean plane. In examples, ‘points’ may be functions where uniformity of convergence can be captured, or binary sequences with applications in computer science, or even subsets of a Euclidean space delivering fractal sets as limits.
Topology goes further, abstracting the notions of continuity and convergence, rendering a teacup and doughnut indistinguishable. A topological space equips each of its ‘points’ with its so-called ‘neighbourhoods’. The few simple principles governing these unlock a robust theory that now pervades the mathematical sciences and theoretical physics.
You will learn the fundamental concepts of completeness, total boundedness for metric spaces, compactness, and the Hausdorff property and metrisability for topological spaces.
From the complicated behaviours of ecosystems to the rapid spread of wildfires, nonlinear dynamics govern many fascinating phenomena around us. They underpin vital technological applications, such as the functioning of chemical reactors and the design of efficient transport networks. Nonlinear systems exhibit a rich variety of behaviours including sudden transitions, chaos and self-organised pattern-formation.
This module will combine theoretical insights with computational approaches to explore the nonlinear world in which we live, introducing key ideas in the geometric theory of dynamical systems and nonlinear partial differential equations. You will uncover how simple rules give rise to intricate structures, why some deterministic systems seem to have probabilistic behaviour, and how nonlinear models help describe real-world problems in diverse areas, such as traffic flow and population dynamics. Topics will range from stability, oscillation and chaos to nonlinear waves and shock formation, revealing the beauty and complexity of nonlinear systems in action.
Building on the statistical techniques explored so far, you will gain an understanding of both the theoretical underpinnings and practical application of frequentist statistical inference. You will then be introduced to an alternative paradigm: Bayesian statistics.
The frequentist perspective views all probabilities in terms of the proportions of outcomes over repeated experimentation and has been the foundation of hypothesis testing and experimental design in years of data-driven science and research. Meanwhile, the increasingly popular Bayesian approach arises directly from Bayes theorem, avoiding hypothetical repeated sampling. As a result, Bayesian statistics is often more intuitive and easier to communicate and naturally takes all forms of uncertainty into account.
With this in mind, you will compare and contrast these two perspectives and their associated tools. You will learn to select and justify an appropriate methodology for inference and model selection, and to reason about the uncertainty in your findings within each paradigm.
Stochastic processes are fundamental to probability theory and statistics and appear in many places in both theory and practice. For example, they are used in finance to model stock prices and interest rates, in biology to model population dynamics and the spread of disease, and in physics to describe the motion of particles.
During this module, you will focus on the most basic stochastic processes and how they can be analysed, starting with the simple random walk. Based on a model of how a gambler's fortune changes over time, it questioned whether there are betting strategies that gamblers can use to guarantee a win. We will focus on Markov processes, which are natural generalisations of the simple random walk, and the most important class of stochastic processes. You will discover how to analyse Markov processes and how they are used to model queues and populations.
Statistics and machine learning share the goal of extracting patterns or trends from very large and complex datasets. These patterns are used to forecast or predict future behaviour or interpolate missing information. Learn about the similarities and differences between statistical inference and machine learning algorithms for supervised learning.
You will explore the class of generalised linear models, which is one of the most frequently used classes of supervised learning model. You will learn how to implement these models, how to interpret their output and how to check whether the model is an accurate representation of your dataset. Lastly, you will have the opportunity to see how these models can be extended to the case of the ‘large p, small n’ question. This phrase refers to the situation in which there are many more variables than there are samples, something which is now commonplace.
Enhancing our curriculum
We continually review and enhance our curriculum to ensure we are delivering the best possible learning experience, and to make sure that the subject knowledge and transferable skills you develop will prepare you for your future. The University will make every reasonable effort to offer programmes and modules as advertised. In some cases, changes may be necessary and may result in new modules or some modules and combinations being unavailable, for example as a result of student feedback, timetabling, staff changes and new research.
Fees and funding
We set our fees on an annual basis and the 2026/27
entry fees have not yet been set.
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
College fees
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small college membership fee which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.
For students starting in 2025, the fee is £40 for undergraduates and research students and £15 for students on one-year courses.
Computer equipment and internet access
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
Study abroad courses
In addition to travel and accommodation costs, while you are studying abroad, you will need to have a passport and, depending on the country, there may be other costs such as travel documents (e.g. VISA or work permit) and any tests and vaccines that are required at the time of travel. Some countries may require proof of funds.
Placement and industry year courses
In addition to possible commuting costs during your placement, you may need to buy clothing that is suitable for your workplace and you may have accommodation costs. Depending on the employer and your job, you may have other costs such as copies of personal documents required by your employer for example.
The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your fee status.
Home fees are subject to annual review, and may be liable to rise each year in line with UK government policy. International fees (including EU) are reviewed annually and are not fixed for the duration of your studies. Read more about fees in subsequent years.
We will charge tuition fees to Home undergraduate students on full-year study abroad/work placements in line with the maximum amounts permitted by the Department for Education. The current maximum levels are:
Students studying abroad for a year: 15% of the standard tuition fee
Students taking a work placement for a year: 20% of the standard tuition fee
International students on full-year study abroad/work placements will also be charged in line with the maximum amounts permitted by the Department for Education. The current maximum levels are:
Students studying abroad for a year: 15% of the standard international tuition fee during the Study Abroad year
Students taking a work placement for a year: 20% of the standard international tuition fee during the Placement year
Please note that the maximum levels chargeable in future years may be subject to changes in Government policy.
Scholarships and bursaries
Details of our scholarships and bursaries for students starting in 2026 are not yet available.
The information on this site relates primarily to 2026/2027 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.
The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.
More information on limits to the University’s liability can be found in our legal information.
Our Students’ Charter
We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. Find out more about our Charter and student policies.
Undergraduate open days 2025
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Most first-year undergraduate students choose to live on campus, where you’ll find award-winning accommodation to suit different preferences and budgets.
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