Key Facts

Subject area: Computer Science
Programme: PhD / MPhil
Duration: PhD – 3 years full-time, 6 years part-time
MPhil – 1 year full-time, 2 years part-time
Starting Date: Any time of the year
Entry Requirements: See here
International entry requirements We accept a range of qualifications from other institutions.
Standard English Language requirements apply. Details here.

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.

 

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 research methods for designing and creating complex systems and products, with a focus on evidence based engineering, user centred design and analytics.

Machine Learning and Computational Intelligence: we research the development and application of machine learning and computational intelligence methods to address biomedical and engineering problems characterised by large volumes of complex data

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:

  • Empirical evaluation of software development techniques, tools and practices
  • Evidence-based software engineering
  • The trade-off between security and usability for e-government services accessed through mobile devices
  • Automating the process of systematic reviews in software engineering
  • Automated detection of spam on social media
  • Visualisation and data analytics for e-health systems
  • Large-scale software testing supported by cloud computing
  • Network analysis of large-scale software systems
  • Knowledge elicitation and knowledge modelling
  • Visualisation support for searching large volumes of text
  • Cloud computing applications
  • Knowledge engineering and data warehousing

 

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

Contact Details

Administrator Lisa Cartlidge
Tel : (+44) 01782 733412
E-Mail : l.j.cartlidge@keele.ac.uk