Overview

PhD and MPhil projects are available across a range of areas including Software and Systems Engineering, Computational Neuroscience and Biomedical Engineering, Evolutionary Systems, Machine Learning and Computational Intelligence.

PhD projects are usually for 3 years (full time) and MPhil projects for 1 year (full time).  Part time study is also possible.  For further information see the Centre for Computer Science Research website.

Research interests

Below is a selection of our current and recent research interests. 

Computational Neuroscience and Biomedical Engineering: we research applications and methods of computational modelling and analysis with the aim to understand how neural systems work and to design engineering solutions for biomedical problems that involve abnormal or lacking neural control.

Evolutionary Systems: we work on the development and analysis of neuroevolutionary systems to advance understanding of the capacity of evolutionary processes to generate increasingly intelligent behaviours in virtual creatures.

Software and Systems Engineering: we carry out research in the areas of complex systems, computational systems, knowledge modelling, human-computer interaction (HCI), user-centred design (UCD), ‘big data’ analytics, e-Learning and biological system design. We also have research interests in computer vision, cultural informatics, wearable systems and health technologies. Together our research themes have applications and strong relevance to health, education and smart energy.

Machine Learning and Computational Intelligence: AI (deep learning, reinforcement learning, federated learning) and data analytics for various IoT and smart energy and smart city applications; fog/edge computing; digital twin.

Research topics

Below is a selection of our current and recent research topics. In general we are open to investigate any research topic in our areas of interests.

Computational Neuroscience and Biomedical Engineering:

• Biologically inspired navigation for mobile robots
• Computational modelling and imaging-based analysis of neuromodulation
• Virtual reality tools to support upper limb prosthesis control
• Automated assessment of the activity status of Parkinson’s Disease patients
• Development and application of hardware and software implementations of neurons
• Analysis of high-resolution EEG data

Evolutionary Systems:

• Modelling the evolution of complexity in organisms
• Computational modelling and analysis of the evolution of antimicrobial resistance in bacteria
• Modelling the role of environmental risk factors in the evolution of cooperation
• Computational modelling and analysis of social learning
• Modelling the evolution of animal and human populations
• Realistic 3D simulation and analysis of the evolution of creative behaviour
• Network analysis of protein interaction systems to support drug discovery
• Analysis and modelling the evolution of social networks
• Measurement, analysis and modelling of communication complexity

Software and Systems Engineering:

• Visualisation and data analytics
• Engineering complex systems simulation>
• Synthetic and systems biology
• Human-computer interaction (HCI) and system design
• User-centred design (UCD) and usability of health systems and mobile apps
• Computer Science Education including Learning Analytics, and e-Learning design
• Wearable, assistive and health technology design and evaluation
• Smart energy systems, visualization and Internet-of-Things (IoT)
• Knowledge elicitation and Knowledge modelling
• Security, system usability and cloud computing applications
• Machine learning, computer vision and automated detection
• Artificial intelligence (AI) and ethics
• 3D visualisation, VR, AR and reconstruction of cultural artefacts
• Evidence-based software engineering
• Automating the process of systematic reviews in software engineering

Machine Learning and Computational Intelligence:

• Machine learning methods for detection and analysis of faults in reinforced concrete
• Computational intelligence for forensic analysis
• Large-scale marketing data analysis supported by cloud computing
• Microscopy data analysis
• Estimation of the risk of stock portfolios
• Neural network approximation of high-dimensional functions
• Deep learning classification of biomedical data
• Reservoir computing with random projections
• Bayesian analysis of swarm optimisation
• Classification of mitochondrial proteins