1 year full time, 2 years part time
Starts September 2017

Intelligent and Adaptive Systems

The study of natural and artificial intelligent and adaptive systems is at the heart of rapidly developing areas ranging from artificial intelligence and autonomous robotics to neuroscience, consciousness and cognitive science.

This MSc prepares you for research and development in intelligent and adaptive systems, covering theoretical issues and practical techniques for their design and implementation.

You can choose to organise your studies around these themes:

  • artificial intelligence and cognitive modelling
  • robotics and autonomous systems
  • data science, machine learning and natural language processing
  • computational biology and consciousness science.

Key facts

  • Ranked one of the top UK universities for research in computing in the most recent Research Excellence Framework (REF) – all aspects of our research environment were rated as world leading or internationally excellent.

How will I study?

You’ll study a number of core modules and options. You’ll also work on a supervised dissertation.

If you’re new to programming, you have the opportunity to develop your Java skills. If you’re an experienced programmer, you can enhance your skills by studying advanced software engineering.

The course provides you with solid foundations in key areas such as adaptive systems, data science, complex systems and evolutionary computing. You also have the opportunity to take a range of options in cutting-edge areas such as the science of consciousness, computer vision and natural language processing.

You're assessed through:

  • coursework and essays
  • unseen examinations
  • programming projects
  • group projects and presentations
  • a 12,000-word dissertation.

MSc Project

You’ll complete a substantial MSc project, which is often practical as well as theoretical. You may have opportunities to work with an industrial partner.

Some of our previous students’ project work has led to journal and conference publication, giving them a head start in their careers.

The project demands individual responsibility and promotes skills deveopment in:

  • project management and planning
  • resourcing and scheduling
  • documentation and communication
  • critical awareness and creative thinking.

You’re encouraged to seek a project with a commercial/industrial flavour. Finding an industrial sponsor or host is fine, though you’ll still need an academic supervisor.

Full-time and part-time study

You can choose to study this course full time or part time. For details about the part-time course structure, contact us at

What will I study?

  • Module list

    Core modules

    Core modules are taken by all students on the course. They give you a solid grounding in your chosen subject and prepare you to explore the topics that interest you most.

    • Mathematics and Computational Methods for Complex Systems

      15 credits
      Autumn Teaching, Year 1

      This module provides a foundation in mathematical and scientific computing techniques used widely in artificial intelligence, artificial life and related fields. The material covered in this module will facilitate the study of a number of options on other MSc courses at a deeper level than would be possible without it. In particular, it is a prerequisite for the Neural Networks and Computational Neuroscience modules. Coursework is based around Matlab packages.

      Topics include:

      • vectors and matrices
      • differential calculus
      • numerical integration
      • probability and hypothesis testing
      • dynamical systems theory.
    • Adaptive Systems

      15 credits
      Spring Teaching, Year 1

      During this module you will gain familiarity with a number of different approaches to modelling and understanding adaptive processes in natural and artificial systems. This module covers recent work in AI that is geared towards understanding intelligence, both in natural and artificial systems, in terms of the generation of adaptive behaviour in autonomous agents acting in dynamic uncertain environments. Adaptation is studied at both the evolutionary and the lifetime scale.

      Topics include: cybernetic roots of AI; frameworks for adaptive behaviour; evolutionary theory; genetic algorithms; somatic adaptation; classical control theory; fuzzy control; autonomous robots (behaviour based, subsumption architecture, evolutionary robotics); reinforcement learning; Q-learning; learning classifier systems; reinforcement learning in neural networks; applied reinforcement learning; and methods of analysis and modelling.

    • Machine Learning

      15 credits
      Spring Teaching, Year 1

      In this module, you explore advanced techniques in machine learning.

      You use a systematic treatment, based on the following three key ingredients:

      • tasks
      • models
      • features.

      As part of the module, you are introduced to both regression and classification, and your studies emphasise concepts such as model performance, learnability and computational complexity.

      You learn techniques including:

      • probabilistic and non-probabilistic classification and regression methods
      • reinforcement learning approaches including the non-linear variants using kernel methods.

      You are also introduced to techniques for pre-processing the data (including PCA).

      You will then need to be able to implement, develop and deploy these techniques to real-world problems.

      In order to take this module, you need to have already taken the 'Mathematics & Computational Methods for Complex Systems' module (817G5), or have taken an equivalent mathematical module or have equivalent prior experience.

    • Intelligent and Adaptive Systems Project

      60 credits
      Spring & Summer Teaching, Year 1


    Alongside your core modules, you can choose options to broaden your horizons and tailor your course to your interests.

    • Advanced Software Engineering

      15 credits
      Autumn Teaching, Year 1

      In this module, you study modern approaches to large-scale software production.

      You start by reviewing the key concepts in the whole life-cycle of a software product, such as:

      • requirement analysis
      • software architecture and design
      • implementation
      • quality assurance
      • maintenance activities.

      Following this review, you investigate modern software engineering technology, such as:

      • version control
      • build automation
      • testing
      • logical approaches to specification
      • verification of programs and domain-specific languages.

      As part of this module, you undertake team-based coursework, which involves the production of a significant software system.

    • Applied Natural Language Processing

      15 credits
      Autumn Teaching, Year 1

      Applied Natural Language Processing concerns the theory and practice of automatic text processing technologies.

      In this module, you study core, generic text processing models, such as:

      • tokenisation
      • segmentation
      • stemming
      • lemmatisation
      • part-of-speech tagging
      • named entity recognition
      • phrasal chunking
      • dependency parsing.

      You also cover related problems and application areas, such as:

      • document classification
      • information retrieval
      • information extraction.

      You gain hands-on experience with the practical aspects of this module through weekly laboratory sessions.

      As part of this, you make extensive use of the Natural Language Toolkit, which is a collection of natural language processing tools written in the Python programming language.

      Your seminars in this module provide in-depth discussion of a number of important issues that arise when developing natural language processing tools, including:

      • experimentation and hypothesis testing
      • advanced data smoothing techniques
      • domain adaptation
      • topic modelling
      • active learning
      • generative versus discriminative learning
      • semi-supervised learning.
    • Artificial Life

      15 credits
      Autumn Teaching, Year 1

      This module provides you with an introduction to the new field of artificial life. The module has a dual focus: first in bringing computing ideas from biology to AI that are useful in synthesising hardware and software-lifeline artefacts, and secondly using computational tools for testing ideas in biology.

      Topics that you will study include: cellular automata and random Boolean networks; models of growth and development; introduction to evolutionary algorithms; dynamical system approaches to cognition; coevolution; fitness landscapes; and information theory and life.

    • Intelligence in Animals and Machines

      15 credits
      Autumn Teaching, Year 1

      The module will help yopu develop an understanding of what it means for an animal or a machine to behave intelligently, and how brain and behavioural systems are adapted to enable an animal to cope effectively within its environment. We consider diverse aspects of intelligence including navigation and motor control, numerical, language, memory and social skills. We ask how these are related to one another and how they are matched to the particular needs of animals and machines.

    • Object Oriented Programming

      15 credits
      Autumn Teaching, Year 1

      You will be introduced to object-oriented programming, and in particular to understanding, writing, modifying, debugging and assessing the design quality of simple Java applications.

      You do not need any previous programming experience to take this module, as it is suitable for absolute beginners.

    • Real-World Cognition

      15 credits
      Autumn Teaching, Year 1

      This module aims to enable you to recognise the achievements and utility of cognitive science and to apply its models and methods to real-world problems. Applications of cognitive science abound in the real world.

      For example, principles derived from cognitive science are applied to the design of information displays, educational technologies and safety equipment, amongst other things. The module provides a framework for characterising different types of problem. Knowledge of research findings from cognitive studies of language, decision making, reasoning and problem solving can help people make better decisions, make them less susceptible to the bogus claims of some advertisements and to help them adopt a more rational stance in their perceptions of risk (e.g. in the context of gambling, `stranger danger' and medical screening programmes).

      Studies of complex problem solving give us insight into how expert performance differs from that of novices and how, for example, 'everyday' calculations in shops, markets and other real-world contexts differ from similar activities in formal educational settings. Understanding how language and cognition interact shows why some kinds of knowledge is difficult to acquire. Studies of human error show how everyday mistakes and slips occur and how they may be avoided or lessened. These are examples of the kinds of topics that can be approached from a cognitive science perspective.

    • Advanced Natural Language Processing

      15 credits
      Spring Teaching, Year 1

      Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module.

      You will develop your knowledge and understanding of key topics including:

      • word sense disambiguation
      • vector space models of semantics
      • named entity recognition
      • topic modelling
      • machine translation

      Seminars will provide in-depth discussion of research papers related to the key topics and also general issues that arise when developing natural language processing tools, including:

      • hypothesis testing
      • data smoothing techniques
      • domain adaptation
      • generative versus discriminative learning
      • semi-supervised learning

      Labs will provide the opportunity for students to improve their python programming skills, experiment with some off-the-shelf technology and develop research skills.

    • Image Processing

      15 credits
      Spring Teaching, Year 1

      You will cover topics including:
      • introduction to machine vision and relation to image processing
      • camera technologies, lenses for machine vision, image formation and resolution, display technologies
      • image acquisition hardware
      • histogram manipulations
      • linear invariant systems in two dimensions
      • the convolution operation and its discrete implementation as mask operators
      • first and second order differential edge detection operators, edge-filling techniques, Hough transform
      • the 2D Fourier transform and frequency domain filters, 2D correlation 
      • scene segmentation methods and region filling
      • pattern recognition techniques, shape descriptors, Fourier descriptors, template matching
      • examples of machine vision systems in industry.
    • Intelligent Systems Techniques

      15 credits
      Spring Teaching, Year 1

      This module will introduce you to the range of knowledge representation techniques used in contemporary Artificial Intelligence, and give you an understanding of their various strengths and weaknesses.

    • Neuroscience of Consciousness

      15 credits
      Spring Teaching, Year 1

      Consciousness is one of the last remaining frontiers of scientific exploration, and theories and methods in neuroscience are at the front line of this endeavour. Topics covered in this module include: measuring and studying consciousness; states of consciousness (including wake, dreaming, hypnosis and vegetative state); visual consciousness (including the different roles of visual cortex and fronto-parietal network; blindsight and neglect as disorders of visual awareness); implicit learning and meta-knowledge; psychiatric disturbances of consciousness (eg hallucinations, depersonalisation); interoceptive awareness; consciousness and cortical plasticity (examples of synaesthesia, phantom limb and sensory substitution); computational models of consciousness; biological models of consciousness; and evolutionary approaches to consciousness.

Entry requirements

An upper second-class (2.1) undergraduate honours degree or above. Applicants typically have a background in a scientific or technical subject or other disciplines, including computing and cognitive subjects (such as neuroscience, psychology, linguistics or philosophy), requiring either numeracy or computer literacy. Students from other backgrounds who can demonstrate numeracy or computer literacy will also be considered.

English language requirements

Standard level (IELTS 6.5, with not less than 6.0 in each section)

Find out about other English language qualifications we accept.

English language support

Don’t have the English language level for your course? Find out more about our pre-sessional courses.

Additional information for international students

We welcome applications from all over the world. Find out about international qualifications suitable for our Masters courses.

Pre-Masters in Computing

Need to boost your academic skills for your taught course? Find out more about our Pre-Masters in Computing.

Visas and immigration

Find out how to apply for a student visa

Fees and scholarships

How much does it cost?


Home: £9,250 per year

EU: £9,250 per year

Channel Islands and Isle of Man: £9,250 per year

Overseas: £18,750 per year

Note that your fees may be subject to an increase on an annual basis.

How can I fund my course?

Postgraduate Masters loans

Borrow up to £10,280 to contribute to your postgraduate study.

Find out more about Postgraduate Masters Loans


Our aim is to ensure that every student who wants to study with us is able to despite financial barriers, so that we continue to attract talented and unique individuals.

Chancellor’s Masters Scholarship (2017)

Open to students with a 1st class from a UK university or excellent grades from an EU university and offered a F/T place on a Sussex Masters in 2017

Application deadline:

1 August 2017

Find out more about the Chancellor’s Masters Scholarship

Jan Metzger Scholarship for MSc in Intelligent and Adaptive Systems (2017)

£6,000 fee waiver for the MSc in Intelligent and Adaptive Systems.

Application deadline:

1 July 2017

Find out more about the Jan Metzger Scholarship for MSc in Intelligent and Adaptive Systems

Pegge Scholarship for MSc in Intelligent and Adaptive Systems (2017)

The £3,000 Pegge Scholarship is awarded annually to postgraduate students taking the MSc in Intelligent and Adaptive Systems.

Application deadline:

1 July 2017

Find out more about the Pegge Scholarship for MSc in Intelligent and Adaptive Systems

Sussex Graduate Scholarship (2017)

Open to Sussex students who graduate with a first or upper second-class degree and offered a full-time place on a Sussex Masters course in 2017

Application deadline:

1 August 2017

Find out more about the Sussex Graduate Scholarship

Sussex India Scholarships (2017)

Sussex India Scholarships are worth £3,500 and are for overseas fee paying students from India commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex India Scholarships

Sussex Malaysia Scholarships (2017)

Sussex Malaysia Scholarships are worth £3,500 and are for overseas fee paying students from Malaysia commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Malaysia Scholarships

Sussex Nigeria Scholarships (2017)

Sussex Nigeria Scholarships are worth £3,500 or £5,000 and are for overseas fee paying students from Nigeria commencing a Masters in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Nigeria Scholarships

Sussex Pakistan Scholarships (2017)

Sussex Pakistan Scholarships are worth £3,500 and are for overseas fee paying students from Pakistan commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Pakistan Scholarships

How Masters scholarships make studying more affordable

Living costs

Find out typical living costs for studying at Sussex.


Research in the Department of Informatics is split into four groups. 

  • Cognitive Language Processing Systems

    The research of this group addresses the science and engineering of complex systems for cognitively demanding, and data- and language-intensive domains, including the integration of methods from cognitive science, natural language engineering and machine learning.

    Prof John Carroll
    Professor of Computational Linguistics

    Research interests: Computational Linguistics, Computational/Corpus Linguistics, Machine Learning (AI), Medical Informatics, Natural Language Processing

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    Prof Peter Cheng
    Professor of Cognitive Sciences

    Research interests: Cognitive Science, Human computer interaction, Knowledge visualisation / information visualisation / visual analystics, Tactile graphics - cognitive science of, User-authentication - cognitive biometric

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    Dr Bill Keller
    Senior Lecturer in Artificial Intelligence

    Research interests: Computational Linguistics, Computational/Corpus Linguistics, Linguistics, Machine Learning (AI), Natural Language Processing, Probabilistic Methods, Semantics And Pragmatics

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    Dr Novi Quadrianto
    Senior Lecturer in Machine Learning

    Research interests: Bayesian Methods, Computer Vision - Machine Learning, Ethical Machine Learning, Kernel Methods, Machine Learning (AI), Optimisation (AI), Probabilistic Methods, Time Series

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    Prof David Weir
    Professor Of Computer Science

    Research interests: Computational Linguistics, Data Science, Natural Language Processing

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    Dr Sharon Wood
    Senior Lecturer in Computer Science & Artificial Intelligence

    Research interests: Artificial Intelligence, Cognitive Modelling, Cognitive Science

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  • Evolutionary and Adaptive Systems (EASy)

    The EASy group has been internationally prominent since it was established in the early 1990s. It is concerned with the interfaces between the biological and computational sciences, particularly with reference to furthering understanding of brains and minds.

    Dr Luc Berthouze
    Reader in Informatics

    Research interests: Biomedical Signal Processing, Computational Neuroscience, Developmental Robotics, EEG, EMG, Motor Control, Network Theory and Complexity, Neuronal network, Nonlinear Dynamics and Chaos

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    Prof Maggie Boden
    Research Professor of Cognitive Science

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    Dr Christopher Buckley
    Lecturer In Neural Computation

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    Dr Ron Chrisley
    Reader in Philosophy

    Research interests: Artificial Intelligence, Cognition, Cognitive Science, Consciousness, Language & Philosophical Logic, Logic, Philosophy, Philosophy Of Mind, Philosophy of Science & Mathematics, & Mathematical Logic, Robotics

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    Prof Phil Husbands
    Research Professor Of Artificial Intelligence

    Research interests: Adaptive Systems, Artificial Intelligence, artificial life, Bio-inspired Neural Computing, Bio-inspired Robotics, Complex System Design, Computational Neuroscience, Digital Art & Design, Evolutionary Computation, evolutionary robotics, History of Science/Medicine/Technology, Machine Learning (AI), Mobile Robots, Nervous system, Optimisation Problems, Systems neuroscience

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    Prof Thomas Nowotny
    Professor Of Informatics

    Research interests: Biomimetics, Chemical Sensing, Computational Neuroscience, Dynamic Clamp, Electronic Nose, GPU Computing, High Performance Computing, Insects, Ion channels, Machine Learning (AI), Neural networks, New Computing Paradigms, Olfaction, Robotics, Systems neuroscience

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    Dr Andy Philippides
    Reader in Informatics

    Research interests: computational biology, Computational Neuroscience, Computer Vision & Image Processing - Pattern Recognition, Evolutionary Computation, insect navigation, navigation, Robotics

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    Prof Anil Seth
    Professor of Cognitive & Computational Neuroscience

    Research interests: Cognitive Neuroscience, Computational Neuroscience, Consciousness, EEG, Neuroimaging, neuropsychiatry, Neuropsychology, Psychology, Time Series, Virtual Reality

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    Dr Chris Thornton
    Lecturer in Computing Science

    Research interests: Information Theory, Predictive Processing, Theoretical Cognitive Science

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    Dr Sharon Wood
    Senior Lecturer in Computer Science & Artificial Intelligence

    Research interests: Artificial Intelligence, Cognitive Modelling, Cognitive Science

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  • Foundations of Software Systems

    This group is interested in the theory and practice of future computation and communication. We:

    • build mathematical theories of computation
    • design and evaluate distributed applications and services
    • model and analyse data representing system configurations, social networks, trust and provenance.

    Dr Martin Berger
    Lecturer in Foundations Of Computation

    Research interests: Automata Theory, Compiler Theory, Compilers, Computer Systems Security, Concurrency, Cryptography, Domain Specific Languages, Formal Methods, Formal Verification, Foundations of computation, Functional Programming, Just-In-Time Compilers, Logic, Logic for Computer Science, Meta-Programming, Network Security, Programming Languages, Programming Languages - Concurrent, Programming Languages - Distributed, Proof Assistants, Proof Theory, Semantics of Programming Languages, Software Engineering, Software Specification, Software Verification, Theorem Provers

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    Dr Ian Mackie

    Research interests: Visual programming languages

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    Dr George Parisis

    Research interests: Data Centre Networking and Storage, Information-Centric Networking, Network Management, Opportunistic, Delay-Tolerant Networking, Software-Defined Networking and Software Verification

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    Dr Bernhard Reus
    Senior Lecturer in Computer Science & Artificial Intelligence

    Research interests: Computational Complexity, Computer science, Foundations of computation, Software Verification

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    Dr Peter Schrammel
    Lecturer in Computer Science

    Research interests: Abstract Interpretation, Abstraction, Embedded systems, Formal Verification, Hardware/Software Co-verification, Model Checking (Computing), Model-driven Software Eng, Real-time Software Systems, Satisfiability Modulo Theories, Software Engineering, Software Evolution, Software Quality, Software Safety, Software Security, Software Testing, Software Verification, Static Analysis

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    Prof Ian Wakeman
    Professor of Software Systems

    Research interests: Communications networks, Datacenter Networking and Storage, delay tolerant networks, Distributed computing, Mobile Computing

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  • Interactive Systems

    This group is concerned with the interfaces between humans and digital technology. We investigate interaction in the broadest sense, and consider it in relation to both traditional desktop-based technology and also more recent digital technologies – including mobile, immersive, ubiquitous and pervasive computing.

    Dr Natalia Beloff
    Senior Lecturer in Software Engineering

    Research interests: Big Data Analytics, Business models for Digital innovation, E-Business Models, Internet of things, Medical Informatics, Numerical Analysis, Remote Sensing & Earth Observation

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    Dr Judith Good
    Reader in Informatics

    Research interests: Autism Spectrum Disorders, Game Based Learning, Game Creation for Learning, Learning, Learning Programming, Mobile Computing, Multimedia, Simulations for Learning, technology for autism

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    Dr Kate Howland
    Lecturer In Interaction Design

    Research interests: End-user programming, Game Based Learning, Game Creation for Learning, Human computer interaction, Interaction design, Novice programming, Participatory Design, Technology Enhanced Learning

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    Prof Ann Light
    Professor of Design & Creative Technology

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    Dr Paul Newbury
    Senior Lecturer In Multimedia Systems

    Research interests: Technology Enhanced Learning, Virtual Prototyping

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    Dr Marianna Obrist
    Reader in Interaction Design

    Research interests: Interaction design

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    Dr Phil Watten
    Media Technology Manager

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    Dr Martin White
    Reader in Computer Science

    Research interests: 3D Reconstructions, Blockchain Applications, Digital Heritage, Healthy Living Applications

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Graduate destinations

92% of students from the School of Engineering and Informatics were in work or further study six months after graduating. Recent Informatics students have gone on to jobs including:

  • games lab manager, Ubisoft
  • front end developer, Brandwatch
  • UX designer, American Express.

(HESA EPI, Destinations of Post Graduate Leavers from Higher Education Survey 2015)

Your future career

Our students are highly employable, with 95% of recent graduates’ job roles being at professional or managerial level.

Our graduates have gone on to careers in software development, systems analysis and technical communication. A high proportion of our graduates go on to undertake research degrees at Sussex and other prestigious universities.

Employers of our graduates include:

  • IBM
  • Microsoft
  • Sony
  • Siemens
  • American Express
  • Sega.

Working while you study

Our Careers and Employability Centre can help you find part-time work while you study. Find out more about career development and part-time work

“I’m now working on the Bayesian brain hypothesis and predictive processing – the MSc was invaluable in preparing me for research-based work.” Manuel BaltieriAssociate Tutor and PhD student