Computing Sciences Postgraduate Research - PhDs and Studentships


Attendance
Full Time/ Part Time
Award
PhD by Research, MSc (Res), MPhil (Res)



In the School of Computing Sciences we research the application of computational techniques to diverse areas, collaborating with multi-national companies on research that is relevant to the needs of now and the future.

Overview

Our research is organised into five laboratories: Colour & Imaging; Computational Biology; Smart Emerging Technologies; Data Science and Statistics; Graphics, Vision and Speech. 

The Colour & Imaging Lab carries out research at the interface of Computer Vision, Perception and Physics/Engineering. Research areas include (1) image pipelines research at the interface between computer vision and perception where we, for example, develop the image processing on mobile phones (2) Computational photography - which straddles computer vision and physics -where we develop new novel camera architectures including RGB and Near Infrared imaging and (3) mixing perception with engineering we consider how the spectral composition of the visual environment can be designed to help people see better. Core computer science is the heart of what we do and we have expertise in efficient algorithm design, machine (including deep) learning, signal processing, colour processing (e.g. for the creative industries), image fusion, medical imaging, remote sensing and environmental monitoring. The Colour & imaging lab is equipped with SLR cameras, a high dynamic range display, a spectraradiometer, a spectrally tunable illuminant, and several GPU workstations for deep learning. The group has received substantial funding from the EPSRC (UK government), the EU and from industry (Apple and Hewlett Packard amongst others). The Colour & Imaging Lab is preeminent across the university in commercialising its research. It has spun out 4 companies and one, Imsense Ltd, was acquired by an industry major in 2010. 

The Computational Biology Laboratory focuses on research spanning the biological hierarchy, from genome through to ecosystem. The laboratory provides an interdisciplinary environment for research and education, specialising in the computational and mathematical sciences. Areas of research include biological pattern recognition, protein structure, imaging, RNA bioinformatics, growth and development, phylogenetics, medical bioinformatics and systems biology. In addition to carrying out internationally-leading research with national and international partners, the laboratory has strong links with the School of Biological Sciences, The School of Environmental Sciences, The Earlham Institute, The Sainsbury Laboratory, The Quadrum Institute, The John Innes Centre, and the Norfolk and Norwich University Hospital. 

Computer Vision includes among others, aspects of image processing, machine learning, statistical pattern recognition and in some applications also certain aspects of human vision. The three main research areas in the Vision Lab are Lip Reading, Human Motion Analysis and Security Analysis. The Computer graphics group conducts high quality research into GPU programming, scientific visualisation, haptics, medical simulation, urban modelling and historic reconstruction in close collaboration with other research scientists and with potential end-users of the technology. Examples include School of History at UEA, Norfolk and Norwich University Hospital, John Innes Centre (Norwich Research Park) and University College London. The Speech Group is active in fundamental research into speech processing algorithms (e.g. speech recognition in noise, speech enhancement, speaker adaptation, confidence measures for speech recognition) and development of applications of speech processing (e.g. call-routing, recognition of speech transmitted using VOIP, dysarthric speech) for many years. The group has also been active in developing the use of avatars for sign-language, and the research into avatar speech animation is developing avatars that are capable of expressive speech. There are collaborations with Apple and Disney Research as well as with many small companies.  

The Data Science and Statistics Laboratory combines researchers from machine learning, data mining, statistics and actuarial science. Members are developing analysis approaches for “Big Data” processing, linkage, model selection and analysis, aimed at large and complex social and health databases and real-time data.  They specialise in the analysis of big administrative databases of health and educational data. The work is funded by ESRC, by medical research bodies and by the Institute and Faculty of Actuaries. We also have a long-standing collaboration with Aviva. The group is also funded by the EPSRC and Turing institute to conduct research into time series classification. Algorithms they have recently developed are currently the best known in the world at this task and are being applied in collaboration with the Quadrum Institute, Imperial College and the Vermont Energy Corporation to a range of real-world problems, with the potential for genuine societal and economic impact.  

The Smart Emerging Technologies Lab will investigate research which aims to develop more proactive, intelligent, adaptive or autonomous systems that can learn, adapt and make decisions without the need for human control. SET research will inform and contribute to a new level of learning and intelligence where, for example, telecommunications networks can observe and predict the status of the systems that carry different types and classes of traffic (e.g. HD video, IP TV, medical applications, secure traffic, etc.) and adapt behaviours of the network protocols and connected devices for flow control, congestions control, queuing and routing accordingly. Related areas of previous, current and future interest include: Sufficient Networking for Sufficient Sensing, Machine Learning and AI, Robotics and Autonomous Systems, Pervasive Vision and Imaging (e.g. Medical Imaging and Creative), Resilience in Network Design for IoT Deployments, Power-Harvesting and Energy-Aware Dynamic Networks, Network Capacity Planning, Resource Allocation and Management, Nano Data Centres and Fog-edge Computing, Converged Network Access/Backhaul for IoT, Scale IoT Deployments for Connected Services (e.g. Transport, Environment Health, AgriTech, Supply Chain), FCAPS for Resource Constrained Cloud Networking, Emergency Response Communications, UAVs for Communications Bridging and Data Muling, Massive Data Analytics on the Fly across Network Device Boundaries, Wireless Sensor Networks, Cloud & Edge Computing for Next Generation Transport and Health, Converged Communications, SDN and NfV, Applications and Services for Smarter Cities, AI and Predictive Analytics for High-speed Network Management, Network Resilience and Security. 

For more information on our research and current research opportunities visit: 
www.uea.ac.uk/cmp/research 

Each school has funding for some projects; eligibility rules may apply; UEA offers a limited number of ISF scholarships, up to 50 per cent of tuition fees, to excellently qualified overseas (non-EU) students each year; these awards are based on academic merit. 

Take a look at our current PhD and studentship opportunities

Course Modules 2019/0

As a postgraduate research student, your research degree will not contain modules.   

Disclaimer

Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules and regular (five-yearly) review of course programmes. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, there will normally be prior consultation of students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff or sabbatical leave. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will endeavour to inform students.

Entry Requirements

Entry Requirement

To be accepted onto a doctoral degree, the standard minimum academic entry requirement is either at least a 2:1 from a UK undergraduate degree, a Master’s degree, or equivalent. Some research programmes might have higher requirements and the possibility of more specific requirements. Please take a look at our current PhD opportunities or our Faculty Graduate School and School pages to find out more.    

 

How to Apply

Please take a look at our information on how to apply.

    Next Steps

    We can’t wait to hear from you. Just pop any questions about this course into the form below and our enquiries team will answer as soon as they can.

    Admissions enquiries:
    admissions@uea.ac.uk or
    telephone +44 (0)1603 591515