| Four 3 year, fully funded PhD studentships are available on a range of exciting research projects at the Computational Intelligence Research Group (CIRG) at Bournemouth University, United Kingdom.
The students will be joining the Computational Intelligence Research Group (CIRG) and will be primarily based in the School of Design, Engineering & Computing in Bournemouth but in 3 of the projects will also have an opportunity to frequently visit and work in the top R&D labs of such companies as British Telecommunications Plc, Lufthansa Systems Berlin GmbH or Degussa AG.
The studentships carry a basic remuneration of £12500 pa tax-free (with some possible higher bursaries on specific industry co-funded projects) and payment of tuition fees at home/EU rate. The successful applicants will normally need to be EU citizens though a limited number of studentships is available for outstanding non-EU candidates.
For all projects applicants should have a strong mathematical background and hold a first or upper second class honours degree or equivalent in computer science, mathematics, physics, engineering, statistics or a similar discipline. Additionally the candidates should have strong programming experience using any or combination of C++, Matlab or Java.
For further details please contact Prof Bogdan Gabrys, [email protected] or visit the following www pages: http://dec.bournemouth.ac.uk/staff/bgabrys/PhD_Studentships_2006.html
Interested candidates should follow the application procedure listed on the University of Bournemouth web pages:
http://www.bournemouth.ac.uk/thegraduateschool/phd_studentships/how_to_apply.html
Further details concerning the studentships and application procedure can be also obtained from the School of DEC Research Administrator - Ms Jo Sawyer, Email: [email protected]. Tel: +44 (0)1202 965985
All the following projects are expected to start by September 2006 at the latest but all interested candidates are encouraged to apply as soon as possible.
Project 1: Data Mining and Multi Level Combination for Cancellation Forecasting
Prof Bogdan Gabrys (CIRG), Ms Silvia Riedel (LSB), Prof John Fletcher (BU SM School)
Accurate forecasting of the demand for airline tickets is critical within revenue management applications used by large airlines like Lufthansa. In this project the information stored in the airline Passenger Name Records (PNRs) will be exploited through the use of data mining techniques and used within novel adaptable (multilevel) classifier and forecast combination framework for improvement of the cancellation forecasts and overall demand prediction quality.
This is a collaborative research project between Computational Intelligence Research Group (CIRG) and Lufthansa Systems Berlin (LSB), the world leading IT service provider for the airline and aviation industry. The PhD student will have an opportunity to frequently visit and work in LSB offices in Berlin.
Project 2: Self-adapting and Monitoring Soft Sensors for Process Industry
Prof Bogdan Gabrys (CIRG), Dr Paul Rogers (CIRG), Dr Uwe Tanger (Degussa AG)
This project will be an application driven investigation of the possible exploitation of various computational intelligence and nature-inspired techniques for development of self-aware, -monitoring, -validating and -adapting soft sensors for process industry based on a more general class of locally adaptable and highly flexible predictive models required in industrial environments.
This is a collaborative research project between Computational Intelligence Research Group (CIRG) and Degussa AG, a multinational specialty chemistry company with €11.2 billion turn-over in 2004. As part of the project, the PhD student will have an opportunity to frequently visit and work in Degussa Labs in Germany.
Project 3: Physically Inspired Artificial Learning Models
Dr Dymitr Ruta (BT Intelligent Systems Labs), Prof Bogdan Gabrys (CIRG)
The main aim of this research project is to explore and investigate the tremendous similarities between physical world and artificial intelligence in the context of machine learning in order to find inspirations and design the new breed of nature-inspired classification, clustering and regression techniques that would be capable of learning more efficiently from large sources of uncertain multi-type data and information.
The work will include theoretical and explorative modelling in Matlab and Java addressing variety of problems in quantum computing, thermodynamics of information, Kolmogorov complexity, information uncertainty, kernel methods and many more.
This is a collaborative research project between Computational Intelligence Research Group (CIRG) and British Telecommunications (BT) Intelligent Systems Labs, one the largest industrial R&D labs of this type in UK. The PhD student will have an opportunity to frequently visit and work in the BT Intelligent Systems Labs in Ipswich.
Project 4: Using Computational Intelligence Techniques to Support Incidental Learning
Michael Jones (CIRG), Prof. Bogdan Gabrys (CIRG) and Dr Paul Rogers (CIRG)
Incidental Learning is a term, originating in the Computational Intelligence Research Group at Bournemouth University, which describes a novel approach to providing online learners with a more active role in the learning process. Incidental Learning involves the development of a 'cognitive assistant', which continuously filters a wide range of additional support materials (gathered from a variety of external sources) in a manner which is appropriate to the learner's current cognitive load.
The focus for the proposed research is the design of the filtering process, which will use computational intelligence techniques to create a system which will classify the potential usefulness of the additional support materials, most of which will be unstructured. Statistical, machine learning, and hybrid intelligent techniques will be used in the design of the filtering system. The continuous nature of the filtering process imposes performance constraints, which will also form part of the research.
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