MSc Artificial Intelligence for the Environment
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 | 2:1 degree in a non-related science undergraduate degree, or a 2:1 in any other degree subject and A Level Mathematics at Grade A or equivalent. |
|---|---|
Why study MSc Artificial Intelligence for the Environment at Exeter?
- Study AI, machine learning, geospatial analysis and environmental data science, with pathways to apply skills to climate, sustainability and urban challenges.
- Access to advanced computing labs, geospatial tools, and over 1.2 million digital and print library resources.
- Join a diverse global community of researchers, academics and students tackling UN Sustainable Development Goals through interdisciplinary collaboration.
- Prepare for careers as an Environmental AI Specialist, Climate Data Scientist, or Smart Cities Analyst, with growing demand across industries worldwide.
- Gain hands-on experience working with real environmental datasets and complete a final project addressing pressing global sustainability challenges.
- Engage with the activities offered by the Centre for Environmental Intelligence and learn from researchers across many disciplines.
- Opportunity to undertake a research project with external partner organisations of the Centre for Environmental Intelligence, including Met Office, Plymouth Marine Lab, Natural England, National Trust, Ordnance Survey, and more.
Top 20 in the UK for Computer Science
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects for Computer Science in the Complete University Guide 2027
Long-established partnership with the Alan Turing Institute and home to the Institute of Data Science and Artificial Intelligence
Entry requirements
Applicants are required to have at least a 2:1 degree in a non-related science undergraduate degree, or a 2:1 in any other degree subject and A Level Mathematics at Grade A or equivalent.
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 B3.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
The MSc Artificial Intelligence for the Environment combines advanced technical training with real-world environmental applications. You will study core AI methods including machine learning, statistical modelling, geospatial AI, and environmental data science, building the expertise to analyse and interpret complex datasets. Specialist modules explore topics such as remote sensing imagery, uncertainty quantification, and probabilistic forecasting, equipping you with skills directly relevant to global sustainability challenges.
Throughout the programme, you will apply AI techniques to pressing environmental issues, including climate change prediction, sustainable urban development, and resource management. Hands-on learning is central, with opportunities to work directly with diverse datasets ranging from satellite imagery to human mobility patterns. Your final project allows you to collaborate with researchers on real environmental problems, giving you valuable research experience and industry-relevant outcomes. This unique interdisciplinary approach ensures you graduate prepared to lead in the growing field of AI-driven environmental solutions.
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. Because AI is such a rapidly changing field, the precise modules available to you in future years will vary depending to accommodate cutting-edge research and techniques, staff availability, timetabling and student demand.
Please note that the module information displayed here is subject to change.
150 credits of compulsory modules, 30 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Geospatial AI | 15 | |
| Programming with Python | 15 | |
| AI in Environment | 15 | |
| Research Project | 60 | |
| Learning from Data | 15 | |
| Quantitative Methods and AI for Environmental Challenges | 15 | |
| Introduction to Data Science and Statistical Modelling | 15 | |
COMM043: Geospatial AI
In this module, you will learn how to work and analyse geospatial data using AI methods. You will become familiar working with a range of geospatial data, from remote sensing to human mobility traces, and many others. You will learn how to process, visualise, map and model such data sets in the context of environmental applications.
The aim of this module is to introduce geospatial artificial intelligence techniques that are needed for working with large data sets relevant to environmental challenges. This module will equip you with some of the tools and methods that can be used for studying environmental data sets. You will also be exposed to recent advancements and results in this research area.
COMM109: Programming with Python
This module will introduce students to the fundamentals of constructing software using the Python programming language. You will learn how to decompose problems into components that can be implemented to provide a software solution, as well as how to control program flow and represent data within software. Having learned the fundamentals of Python coding you will be introduced to exception handling, Python classes, and be introduced to principles of software development and testing.
COMM119: AI in Environment
AI is playing a crucial role in addressing environmental challenges by enabling data-driven decision-making and sustainable solutions. In this module, you will study the fundamental concepts of AI and its applications in environmental problems, including for example, geographical data analysis, machine learning, and foundation models for climate. You will study the applications of AI in environment, such as climate change mitigation and biodiversity conservation. You will attend lectures complemented by lab sessions or discussion, where you will apply AI techniques to real-world environmental datasets and analyse case studies. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and fundamental machine learning concepts.
COMM514: Research Project
In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.
This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis and presentation of results.
ECMM445: Learning from Data
Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.
The module will also cover some recent neural network architectures and related learning algorithms.
This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).
MTHM065: Quantitative Methods and AI for Environmental Challenges
This module provides a comprehensive introduction to quantitative approaches for understanding and addressing environmental challenges. You will learn to apply statistical analysis, spatial modelling, and other advanced quantitative techniques to environmental datasets. Using real-world case studies, you will explore issues such as climate change, air pollution, biodiversity loss, and extreme weather events.
The module emphasises developing practical skills to analyse and interpret data, draw meaningful conclusions, and support evidence-based decision-making.
Content is regularly updated to reflect the latest research and methodological advances, ensuring you gain cutting-edge skills relevant to contemporary environmental problems.
MTHM502: Introduction to Data Science and Statistical Modelling
In this module you will be equipped with the tools required to collate, import and manipulate data together with methods for basic inference including probability, sampling variability, confidence intervals. You will be introduced to different types and sources of data and the tools for performing initial data analysis including producing simple graphical summaries of data and more sophisticated methods for visualising structures in data. You will learn the essential mathematical techniques that are required for the implementation and interpretation of statistical and machine learning methods.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Blue Planet | 15 | |
| Introduction to Computer Vision | 15 | |
| Machine Learning | 15 | |
| Social Networks and Text Analysis | 15 | |
| Global Challenges | 15 | |
| Green Planet | 15 | |
| Environmental Remote Sensing | 15 | |
| Spatial Data Science | 15 | |
| Modelling the Weather and Climate | 15 | |
| Climate Change Science and Solutions | 15 | |
| Data Science and Statistical Modelling in Space and Time | 15 | |
| Communicating Data Science | 15 | |
BIOM568: Blue Planet
This module provides you with an opportunity to engage with ocean issues and investigate practical and policy opportunities for change. You will consider the importance of the ocean including the different goods and services that it provides to humans and the planet. You will also evaluate and create solutions for the identified challenges and learn about the processes needed to support a sustainable ocean.
This module, suitable for both science and non-science graduates, introduces fundamental principles of marine research. It provides a solid platform upon which to explore key issues in marine science, including impacts such as climate change, deep sea mining, ocean pollution and aquaculture. Taught sessions from leading marine biologists and policy experts will provide introductions to a range of topics, whilst subsequent interactive discussion sessions and debates will deepen your understanding and develop critical awareness and creative solutions-focussed thinking.
COMM042: Introduction to Computer Vision
How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars).
This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.
ECMM422: Machine Learning
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.
ECMM447: Social Networks and Text Analysis
The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.
The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open-source packages.
The module will be taught in weekly lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material.
GEOM141: Global Challenges
This module will give you an introduction to real-world sustainability challenges and potential solutions, training in problem-solving research and how it can be applied to effect meaningful change. This includes an introduction to earth system science and humans' impacts. You will receive training sessions that will develop core skills such as communication, project management, researching the literature, critical thinking, and presenting.
This module will provide you with an understanding of the global context of sustainability and how this can be downscaled to specific challenges, with an emphasis on how to connect sustainability and systems theory to real-world challenges. You will receive guidance and training on critically assessing a proposed sustainability solution. This will give you first-hand experience of working in wicked problem spaces and the challenges and opportunities they provide. A formative assessment will allow you to develop a short factual 'explainer' film that will effectively communicate a particular global challenge to an online general audience.
GEOM149: Green Planet
The Green Planet module will connect you with the land, the greenscapes that provide our home, our food, filter our water and draw down carbon from the atmosphere. The aim being to provide you with an informed, solutions-focussed understanding of the value held within our planets land-based ecosystems, that are literally a green economy. The module will be presented by an array of experts in the field; both from academia, policy, land-management and industry, linking research-based understanding of nature with practical application. The course will cover the basics from the definition of natural ecosystems, why biodiversity matters to the understanding of ecological baselines and biodiversity in the Anthropocene. But further, it will provide you with the opportunity to develop your understanding of the challenges for conservation and how these interact with zero carbon agendas, how we can value and quantify the natural capital held in ecosystems and the services they provide through considering sustainable land-management for food production. If you want to see the value of our natural land-based environment and hone your thinking towards working to provide sustainable land management practices, then this is the module for you.
GEOM180: Environmental Remote Sensing
In this module, you will learn about how different remote sensing approaches are used to quantify environmental changes, and how these observations can be used to help address global challenges such as the UN Sustainable Development Goals. Through lectures you will develop your understanding of the physical principles behind a range of remote sensing approaches, as well as the strengths and weaknesses of these approaches for a range of applications in environmental science. You will then put this understanding into practice during computer-based coding and data analysis tasks. Learning will be consolidated and extended during regular discussion seminars.
You will have the opportunity to use knowledge and skills acquired during this module in GEOM183 Spatial Data Science.
The overall aims of this module are to:
GEOM183: Spatial Data Science
Module Description:
Spatial data science (SDS) adds multidimensional geographical data including evolving timescales and place-based context to the growing field of data science. Combining spatial data for important geographical problems with methods and tools from data science, GEOM183 provides powerful methods to enhance analytics, visualisation, and problem solving.
Building on GIS, coding, and remote sensing skills (e.g., learned in Term 1), discover advanced spatial approaches that incorporate geography into GIS-based computation. This module will provide deeper understanding how to incorporate spatial characteristics using both standard SDS approaches for exploring globally available datasets as well as learning specialist techniques to turn the environmental data into useful understanding (with examples drawn from Exeter research). Approaches you will use include analysing multidimensional data to discover trends, evaluating causal relationships, and quantifying change. You will also undertake local fieldwork to understand land classification techniques and integration of real-time sensor networks into analysis of landscape system dynamics (building on concepts presented in an optional Term 1 module, GEO3223).
Module Aims:
MTHM023: Modelling the Weather and Climate
This module introduces you to modelling the weather and climate by providing you with an overview of modern weather and climate computational models. Using hands-on computational case studies, you will explore key aspects of mathematical and computational modelling within a simpler model framework. You will look at climate physics in more detail, and study the predictability of the atmosphere.
Prerequisite module: MTH3001 Theory of Weather and Climate
This module will give an introduction to both complex and simple models of weather and climate. Simple models are useful for improving our understanding of the climate system; however, to make detailed predictions requires large and complicated numerical models. In order to have a thorough understanding of the outputs from these models, it is important to have a grounding in the techniques employed.
MTHM054: Climate Change Science and Solutions
This module will expose you to some of the most vibrant frontiers in the science of anthropogenic climate change. It will provide an overview of the natural science underlying projections of future climate change, enabling you to distinguish between what is known with high-certainty from aspects that remain uncertain. These latter ‘Frontiers in Climate Change Science’ will be introduced by a number of international experts from the University and other renowned research institutions (such as the nearby Met Office-Hadey Centre). Though natural science led, the module is interdisciplinary with a focus on understanding the climate change problem as part of the wider sustainability agenda.
The module will equip you with an understanding of the science that underpins projections of anthropogenic climate change, and separate what is well known from what remains unknown. It will help you to understand the frontiers of climate change science, through guest lectures by international experts (from the university, the Met Office and other centres), and through student-led discussions. There will be opportunities for peer-to-peer as well as independent learning, and expectations for you to engage in debate and discussion during module sessions. As part of the module assessment, you will work in interdisciplinary groups to explain a proposed climate change solution through a group presentation.
MTHM505: Data Science and Statistical Modelling in Space and Time
In this module, you will learn how to model data that exhibit correlations over space or time. You will explore both the theoretical foundations and practical implementation of statistical methods for correlated data, including Gaussian processes for spatial data and ARIMA/state-space models for time series.
Through practical exercises and coursework, you will gain hands-on experience applying these methods to real-world datasets, including examples from environmental science and computer modelling. By the end of the module, you will be able to model and analyse correlated data effectively and communicate your results clearly using appropriate software tools.
MTHM507: Communicating Data Science
Critical to every successful academic and industrial career is the ability to communicate data analysis in your area of expertise. Through engaging with and discussing papers and data in this data science specialism we will explore the techniques of reading research/policy papers and presenting on new data analysis developments. You will develop the skills that are essential in a fast-paced and constantly developing environment.
This module aims to introduce you to the important aspects of how to develop your own original consulting/research analyses and how to communicate them effectively. To develop in you the key skills required to stay up-to-date with and communicate your subject knowledge and succeed in the professional and academic environment.
Fees
2026/27 entry
UK fees per year:
£12,900 full-time
International fees per year:
£29,800 full-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
Teaching and assessment
The programme is delivered through a mix of lectures, seminars, tutorials, industrial presentations, case studies, industry visits, computer simulations, project work and a dissertation.
You will develop transferable skills such as management and communication skills, computational techniques, data handling and analysis, problem solving, decision making and research methodology. Many of these will be addressed within an industrial and commercial context.
Personal Tutor
You will be allocated a Personal Tutor who is available for advice and support throughout your studies, along with support and mentoring from graduates who are now in industry. There is also a Postgraduate Tutor available to help with further guidance and advice.
A research and practice-led culture
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. Plus, you’ll 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.
Dr Federico Botta is a Senior Lecturer in Data Science at the University of Exeter, specialising in AI, network theory and computational social science to study human behaviour and transport systems. A former Turing Fellow and ESRC/ADR UK 10DS Fellow, they have collaborated with policy makers at 10 Downing Street and the ONS on data-driven public projects.
Their research focuses on improving transport accessibility and performance through advanced data analysis, while actively supporting policy design and interdisciplinary initiatives that bridge data science, AI and societal impact.
Read more from Dr Federico Botta
Dr Federico Botta
Programme Director for MSc Artificial Intelligence for the Environment
Careers
Graduates of the MSc Artificial Intelligence for the Environment are equipped with a unique combination of technical AI expertise and environmental knowledge, positioning them for careers at the forefront of sustainability and technology. With hands-on experience in machine learning, geospatial analysis, and environmental data science, students are prepared to contribute to solutions for climate change, urban sustainability, and resource management. The programme’s strong industry links and Exeter’s award-winning Career Zone provide tailored support to help students secure roles across government, consultancy, research, and technology sectors.
Graduate Destinations
Alumni have gone on to work as Environmental AI Specialists, Climate Data Scientists, Sustainability Analytics Consultants, Smart Cities Data Scientists, AI Solutions Engineers for Environmental Systems, and Environmental Machine Learning Engineers. Graduates will be able to consider opportunities in public sector agencies, environmental consultancies, tech companies, research institutions, and organisations focused on delivering AI-driven solutions for sustainability challenges worldwide.
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.
