JOB: 2 fully funded PhD positions at Smart Technology Research Centre

GWR PhD Studentships
Smart Technology Research Centre
School of Design, Engineering and Computing
Bournemouth University, United Kingdom


Two 3 year, fully funded PhD studentships are available on exciting
research projects at the Computational Intelligence Research Group
(CIRG) within the Smart Technology Research Centre (STRC) at
Bournemouth University, United Kingdom.

Both projects are research collaborations between Smart Technology
Research Centre from Bournemouth University, Artificial Intelligence
Research Group from Bristol University and a large industrial/
commercial partner which in the first case is the British
Telecommunications plc (BT) and in the second case the Screwfix Direct
Ltd.

The students will be joining the Smart Technology Research Centre
(STRC) and will be primarily based in the School of Design,
Engineering & Computing in Bournemouth but will also be required to
frequently visit and work in the top R&D labs of the commercial
sponsors as well as the other academic partner which provide an
outstanding opportunity to gain a diverse experience of both academic
and commercial environments.

The studentships carry a basic remuneration of GBP 12900 pa tax-free
and payment of tuition fees at home/EU rate. The successful applicants
will normally need to be EU citizens though outstanding non-EU
candidates will also be considered.

For both 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 and very good knowledge of database systems like MS SQL
Server, Oracle, and Sybase.

For further details please contact Prof Bogdan Gabrys, bgabrys at
bournemouth.ac.uk or visit the following www pages:
http://dec.bournemouth.ac.uk/staff/bgabrys/PhD_Studentships_2008.html

Interested candidates should follow the application procedure listed
on the School of Design, Engineering and Computing web pages:
http://dec.bournemouth.ac.uk/research/postgraduate_research.html

Further details concerning the studentships and application procedure
can be also obtained from the School of DEC Research Administrator -
Ms Jo Sawyer, Email: jsawyer at bournemouth.ac.uk. Tel: +44 (0)1202
965985

Both projects are expected to start by September 2008 at the latest
but all interested candidates are encouraged to apply as soon as
possible.

Project 1: Probabilistic modelling of customer behaviour using nature-
inspired hybrid optimisation techniques

This application driven collaborative project with BT Intelligent
Systems Labs, one of the largest R&D labs of this type in UK, has two
primary objectives:
1. To explore, identify, develop and test a family of predictive
models that given historical sequences of customer behaviour would be
capable of delivering a range of soft probabilistic predictions
related to the future evolution of such customer event sequences; and
2. To configure and deploy at least one of the devised models to the
real-time operational trial carried out in the BT call centre in order
to evaluate their performance in the real-life operations and
demonstrate added value both to the customer service provider and the
customers themselves.

Project 2: Robust Adaptive Algorithms for Relational Data Mining

This collaborative project with Screwfix Direct, the UK's largest
direct and online supplier of trade tools, accessories and hardware
products, will investigate the application of relational data mining
to large real-world data sets. In order to address problems related
to: a) large amount of data to be processed; b) the complexity and the
dynamic nature of the relationships; and c) the poor quality of the
data; the project will investigate and develop novel approaches within
the following research themes: a) scalability and algorithm design; b)
learning and adaptive mechanisms; and c) data uncertainty modeling and
propagation.

In practical terms related to the software development the overall
approach of the project will include: a) Implementing robust versions
of proposed relational learning algorithms; b) Developing algorithms
that link to commercial database management systems; c) Adapting
relational learning to handle real world data; and d) Designing user-
friendly interfaces to relational learning algorithms.

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