Curious about studying Advanced Artificial Intelligence MResat University of Sussex? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
MRes - Master of Research
University of Sussex
Full Time
Sep 2026
1 Year
Artificial Intelligence (AI) is rapidly becoming a core technology with a diverse range of applications across science, healthcare and society. This MRes goes beyond basic principles. It offers research-focused training that will equip you with the skills and knowledge for doctoral study, and industrial AI research or engineering roles. This course is for you if you have recently studied a closely related technical degree and have a deep interest in artificial intelligence research methods. You’ll explore: cutting-edge machine learning and bio-inspired AI techniques how AI can be applied in a range of contexts and the potential of implications of this AI research and scientific communication skills. A research project on a topic of your choice will give you the opportunity to develop and demonstrate your skills and understanding. At Sussex, you’ll learn from experts with an established history and reputation in AI. We have recently created Sussex AI, a Centre of Excellence, based in the Department of Informatics and led by members of the AI research group. We have strong links with other interdisciplinary centres such as: Sussex Neuroscience Sussex Digital Humanities Lab Sussex Centre for Consciousness Science. You’ll also benefit from our established industrial collaborations. When you graduate, you’ll enter a workplace with strong employer demand for skilled people in AI and data science.
On this module, you will develop your understanding of recent bio-inspired approaches to AI, including their relevance to neuromorphic computing. The benefits, limitations and open challenges of bio-inspired approaches will be discussed.
On this module, you will develop your knowledge and understanding of recent machine learning technologies: how they can be applied to different tasks, their benefits, limitations and open challenges.
On this module, you will construct and present a proposal for a complex research problem within the domain of artificial intelligence.
This module gives you the opportunity to develop a creative project plan for an AI-related challenge that has a high-level of complexity. It should either lead to theoretical contributions to a subfield of AI or employ advanced AI methods to address interdisciplinary or industrial problems.
On this module, you will look at applications from several broad problem domains – healthcare, environmental and societal – in which artificial intelligence methods have been and can be applied.
On this module, you will develop your skills at scientific communication and understanding, focused on the field of artificial intelligence. It is structured to give you practical experience in several important aspects:
The dissertation project is where you will undertake an in-depth investigation into a particular problem within the world of artificial intelligence.
You will:
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:
This module provides a foundation in mathematical and scientific computing techniques used in various fields, including artificial intelligence, artificial life, data science, and computational neuroscience.
Topics include:
This module teaches the computer science aspects of data science. A particular focus is on how data is represented and manipulated to achieve good performance on large data sets (>10 GBytes) where standard techniques may no longer apply.
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:
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.
This module will provide you with the practical tools and techniques required to build, analyse and interpret big data datasets. It will cover all aspects of the data science process including:
The module will 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, tool-use, language, memory and social skills.
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