EPSRC PhD Studentship at UWE Bristol.
Project: Mathematical Modelling and Artificial Intelligence
applied to Statistical Disclosure Control.
This interdisciplinary PhD programme, based at the
University of the West of England (UWE Bristol), will be
grounded in the fields of Mathematical Modelling and
Artificial Intelligence. It is funded via the EPSRC’s
Mathematical CASE programme, and has been designed to
provide the research student with substantial modelling and
algorithmic knowledge in the areas of mathematical
programming, combinatorial optimisation, evolutionary
algorithms, and statistical analysis. They will also gain
valuable experience and skills from working in two large
organisations with different cultures.
In today’s “Knowledge Economy”, many organisations hold
large amounts of data gathered from a variety of sources,
some of which they wish to publish, sell, or otherwise
exploit and disseminate, whilst respecting the privacy of
individual sources. One such example is the UK’s Office for
National Statistics (ONS), which often needs to protect the
confidentiality of “sensitive” data in published tables,
achieving this via a number of approaches collectively known
as Statistical Disclosure Control (SDC). The sheer size of
the tables means that existing methods are no longer
possible to use, and so there is a need for novel approaches
to this problem based on Artificial Intelligence (AI).
The project concerns the development of new mathematical
models of how accurately a malicious “attacker” can estimate
the value of data in sensitive cells in a table. Building on
this the project will then develop AI techniques for SDC
based on simulated evolution which can be used to create
tables in which confidentiality is protected. The project
will build on an existing collaboration between Bristol UWE
and ONS which has generated experience in this area, and
substantial research expertise in Mathematical Modelling (Dr
Alistair Clark at UWE) and Evolutionary Computation (Dr Jim
Smith at UWE).
This project will be undertaken within Network Analysis &
Optimisation group
(www.cems.uwe.ac.uk/amg/network_analysis.htm) and the the
Artificial Intelligence group (www.cems.uwe.ac.uk/aig) at
UWE in collaboration with the UK Office for National
Statistics (ONS). The student will spend time both at UWE’s
Frenchay campus in Bristol, and ONS, primarily in their
Newport offices. The Artificial Intelligence group is a
lively interdisciplinary group with close links to other
research groups both in the Faculty, regionally, and
internationally, and the student will also benefit from the
support and activities of the Faculty’s Graduate School.
The three and a half-year studentship covers tuition fees
and provides a maintenance stipend of £14,600 per year.
We are looking for a highly motivated candidate with a good
honours degree (minimum 2.i) or Masters in Electronic
Engineering, Computer Science, Operational Research,
Mathematics or a related discipline, have good analytical
and programming skills and a strong interest in mathematical
programming modelling and artificial intelligence. Previous
experience of mathematical modelling tools such as AMPL,
optimisation solvers such as Cplex, and of programming in
C/C++ would be an advantage. The candidate must also have a
positive attitude toward interdisciplinary research and
teamwork. A recognised English language qualification
(minimum IELTS 6.5, TOEFL 600) is required if English is not
the candidate’s first language. For informal enquiries
please contact Dr Jim Smith, preferably by email:
[log in to unmask]
For application forms, please contact the Graduate School
Office, Faculty of Computing Engineering and Mathematical
Sciences via email [log in to unmask] or telephone
(+44) 0117 32 83580.
Please submit applications by 30th May 2007.
--
Dr. Alistair Clark,
Principal Lecturer in Operational Research,
Faculty of Computing, Engineering and Mathematical Sciences,
University of the West of England,
Bristol, BS16 1QY, England.
tel: +44 (0) 117 328 3134
fax: +44 (0) 117 328 2734
e-mail: [log in to unmask]
http://www.cems.uwe.ac.uk/~arclark/
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