Position for Research Assistant

[Guido.Bugmann xtn 2566]

University of Plymouth
School of Computing
Neurodynamics Research Group

Postgraduate Research Assistant

Applications are invited for a University-funded three-year postgraduate
research assistant, to carry out an investigation within a broad range of
topics related to the development of novel, biologically-inspired, neural
network based, learning control systems, with particular application to the
control of autonomous mobile robots. The range of work extends from
theoretical studies of cognition and intelligent behaviour, through
computational modelling of brain function, to the construction of neural
network based controllers. The research will be carried out within the
Neurodynamics Research Group of the School of Computing, further details of
which are given below. The research assistant will be required to register
for a PhD degree (fees are waived for University staff) and to carry out
limited teaching/demonstrating duties.

We are looking for high quality candidates who have, or are in the process
of completing, a first degree or masters degree in a relevant discipline,
eg electronic/mechanical engineering, mathematics, psychology, cognitive
science, who are willing or able to use computational tools for either
simulation or real-time control, and who have a strong interest in pursuing
research in neural networks/systems, adaptive learning systems, and control
systems.

The salary for the post will be on the University Research Assistant scale,
with a salary in the range A39231 to 12756 pa., dependent upon age,
qualifications, experience, etc.

Informal discussions about the post can be held with Dr Guido Bugmann
(e-mail: gbugmann at soc.plym.ac.uk; tel: 01752 232566).

Applications (by mail or email) should comprise a CV, a short description
of interests and the names of 2 referees. Applications should be sent to
Guido Bugmann at the address below, as soon as possible. The position will
stay open until a suitable candidate is found.


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Dr. Guido Bugmann
Neurodynamics Research Group
School of Computing
University of Plymouth
Plymouth PL4 8AA
United Kingdom
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Tel: (+44) 1752 23 25 66 / 41
Fax: (+44) 1752 23 25 40
Email: gbugmann at soc.plym.ac.uk

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The Neurodynamics Research Group - Background Information

The aim of this group is to investigate and develop computational neural
models of brain behaviour in sensory perception, learning, memory and motor
action planning and generation, and to use these models to develop novel
artificial systems for intelligent sensory-motor control, eg of autonomous
robots. The group was started in September 1991 and is led by Professor
Mike Denham. Researchers in the group currently include a postdoctoral
University Research Fellow, Dr Guido Bugmann, who has an international
reputation in the field of neural dynamics, an EPSRC-funded postdoctoral
Research Fellow, Dr Raju Bapi, who was a member of Prof Dan Levine's
research group at the University of Texas, and who has expertise in the
modelling of frontal lobe behaviour. The Group also has one
University-funded Research Assistant and four research students The Group
was also recently expanded by the appointment of a Senior Lecturer in
Artificial Intelligence, Dr Sue McCabe, who was previously at the Royal
Naval Engineering College and has expertise in AI, intelligent control and
intelligent sensing, especially neural network models of auditory
processing. The Group was awarded an EPSRC research grant, starting in
August 1994, to investigate a novel biologically-inspired architecture for
an intelligent control system, which is a collaborative project with
Professor John Taylor and the Centre for Neural Networks at Kings College
London.

So far, the Group have been working on specific parts of the proposed
integrated learning control system and have been able to contribute
significantly to knowledge on visual information processing and on
planning.  As a result of our work over the last year, we have now begun to
define the approach necessary for solving the deep theoretical and
practical problems of integrating the various parts of the proposed system,
based around an "sensory-action" approach to object perception and
recognition and to the learning of spatial maps and adaptive behaviours for
changing control objectives and environments. A novel neural network based
system for control of an autonomous mobile robot has been developed and a
simulation has been constructed using the Cortex-Pro system on a 486 PC.
This simulated system controls currently a real robot with video camera and
provides a practical working example of the basic architecture of the
proposed learning control system. The intention is to build more advanced
and detailed models of individual modules into the system as a result of
parallel conceptual and theoretical research, eg into perception, learning
and motor planning, in the Group.

Recent publications:

"A model for latencies in the visual system"
Bugmann, G. and Taylor J.G. (1993)
Proc. 3rd Conf. on Artificial Neural Networks (ICANN'93, Amsterdam),
Gielen S. and Kappen B. (eds), p.165-168.

"Modelling of the high firing variability of real cortical neurons with
the temporal noisy-leaky integrator neuron model"
Christodoulou C., Clarkson T., Bugmann G. and Taylor J.G. (1994)
Proc. IEEE Int. Conf. on Neural Networks (ICNN'94) part of the
World Congress on Computational Intelligence (WCCI'94), Orlando,
Florida, USA, 2239-2244..

"An artificial neural network architecture for multiple temporal sequence
processing"
McCabe S L and Denham M J (1994)
Proc World Congress on Neural Networks (WCNN'94), San Diego, California,
USA, 738-743.

"Role of short-term memory in visual information processing"
Bugmann, G. and Taylor J.G. (1994)
Proc. of Int. Symp. on Dynamics of Neural Processing, Washington, DC,
USA, 132-136.

"Learning to control intelligently"
Denham, M J (1994)
Proc. IEE Int Conf Control'94, Warwick, UK (plenary paper)

"Route finding by neural net"
Bugmann G, Taylor J G and Denham M J (1995)
in Taylor JG (ed) Neural Networks, Alfred Waller L,
Henley on Thames, pp217-230.

"Robot control using temporal sequence learnin"
Denham M J and McCabe S L (1995)
Proc. World Congress on Neural Networks (WCNN'95), Washington D.C.,
USA (accepted for presentation)

"Segmentation of the auditory scene"
McCabe S L and Denham M J(1995)
Proc. World Congress on Neural Networks (WCNN'95), Washington D.C.,
USA (accepted for presentation)

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