The Computer Science department brings together Computer Scientists and Mathematicians working on a variety of modern-day, interdisciplinary topics including problems of knowledge representation, machine learning, optimisation, image and signal processing. We have our own Beowulf parallel machine and access to the University's EPSRC-funded IBM supercomputer SP3 system.
Applying for an EPSRC studentship:
This webpage lists research themes based in Computer Science. When you submit an application you will be expected to submit a research proposal aligned to a theme listed below. Examples of research projects previously available are included for reference.
Social and Environmental Data Science uses a variety of computational methods (including machine learning, network analysis and text/image processing) to solve problems in a variety of domains. Data science research at Exeter is often linked to the Institute for Data Science & Artificial Intelligence (http://www.exeter.ac.uk/idsai/) and takes advantage of institutional membership of the Alan Turing Institute. Research areas include environmental science, computational social science, digital politics and urban analytics, amongst many others.
Human Dynamics and Urban Systems focus on the problems related to urban living which ranges from traffic modelling, city accessibility, crime modelling and prediction, and more broadly city planning and predictability of human dynamics.
For more information please contact: Prof Ronaldo Menezes (R.Menezes@exeter.ac.uk)
Researchers of the evolutionary computing and optimisation group study and develop evolutionary algorithms, genetic programming, hyperheuristics, swarm intelligence and multi- and many- objective versions of these for problems such as hydroinformatics, bioinformatics, optimisation under uncertainty and interactive evolution.
Examples of projects from previous years include:
- Data-structures for maintaining non-dominated sets on high performance and distributed systems
- Robust automated timetabling
The network science theme focuses on the phenomena, intrinsic properties and real-world applications of complex networks (such as complex systems and human dynamics), which are often inspired by nature and occur in many real-world contexts including social, biological and neural networks.
Examples of projects available in previous years include;
- Quantification and Modelling of Human Mobility Patterns under Partial or Spurious Information
- Towards understanding recurrent neural networks by means of network attractors
Tired of insecure, crashing, or non-privacy aware systems? The Security and Trust of Advanced Systems group researches all aspects of building secure, safe, privacy-preserving, reliable, and trustworthy systems. We focus on methods for building system that are secure, privacy-preserving, and safe “by design”. We work on approaches for analysing the security or safety of advanced systems (e.g., distributed systems using Blockchains, autonomous systems, cyber-physical systems, or systems using ML or AI). The expertise of the group ranges from formal verification (using, e.g., mathematical logic) to more applied approaches using static and dynamic testing or model-driven design.
The high performance computing and networking group investigates the advanced and intelligent computational and networking technologies and the challenges associated with the future Internet, 5G/6G mobile networks, cloud and edge computing, unmanned vehicles, computing and networking systems supported by AI/Machine Learning.
Examples of projects available in recent years include;
- Learning-based Anomaly Detection and Prediction in 5G Mobile Networks
- Learning-to-optimization for network slicing in 5G mobile networks
The machine learning theme (and its subthemes) at Exeter spans the range of data, applications and methodologies from kernel methods to deep neural architectures and reinforcement learning applied to both continuous and discrete, graph-based data.
The activities of the computer vision group include visual attention, autonomous control, collaboration and decision strategies for cooperative robots, deep multi-modal embedding, graph neural networks etc.