Four Postdoctoral Research Fellowships

[listerrj]

               Neural Computing Research Group
               -------------------------------

       Dept of Computer Science and Applied Mathematics

              Aston University, Birmingham, UK



            FOUR POSTDOCTORAL RESEARCH FELLOWSHIPS
            --------------------------------------


    ***  Full details at http://neural-server.aston.ac.uk/  ***


The Neural Computing Research Group has recently been successful in attracting
significant levels of funding from the Engineering and Physical Sciences 
Research Council, and consequently is able to offer 4 full-time postdoctoral 
Research Fellowships. These positions have a nominal start date of 1 October 
1995, although earlier or later start dates can be agreed if appropriate. 



     Validation and Verification of Neural Network Systems 
     -----------------------------------------------------

                       (Two Posts)


One of the major factors limiting the widespread exploitation of neural 
networks has been the perceived difficulty of ensuring that a trained 
network will continue to perform satisfactorily when installed in an 
operational system. In the case of safety-critical systems it is clearly 
vital that a high degree of overall system integrity be achieved. However, 
almost all potential applications of neural networks entail some level of 
undesirable consequence if the network generates incorrect or inaccurate 
predictions. Currently there is no general framework for assessing the 
robustness of neural network solutions or of systems containing embedded 
neural networks. This substantial and ambitious programme will address the 
basic issues involved in neural network validation and verification in the 
context both of stand-alone network solutions and of embedded systems. It 
will develop and demonstrate robust techniques for quantifying the 
reliability of neural network predictions, and will also provide the necessary 
theoretical foundation of a subsequent framework for neural network system 
validation. 

This project will address the theoretical basis for determining valid 
generalisation and error estimates for neural network predictions, and will 
aim to understand the impact of uncertainties in network predictions on 
overall system performance in the context of embedded applications. It will 
also demonstrate the use of these techniques for validation of network 
solutions through case studies based on real-world applications and data, 
which will be provided by industrial collaborators. 

Potential candidates should be mathematically and computationally competent 
with a background either in artificial neural networks or a relevant field. 
These posts are tenable for two years in the first instance, with a possible 
extension for a further three years.


    Nonstationary Feature Extraction and Tracking for the Classification
    --------------------------------------------------------------------

              of Turning Points in Multivariate  Time Series
              ----------------------------------------------

                            (One Post)

The project is aimed at extracting information from nonstationary, nonlinear 
time series. Real-world examples which have motivated the proposal include:
the early classification of highs, lows and sideways drift in financial global 
bond markets; the forecasting of characteristic clustering such as peaks and 
troughs in consumer driven electricity load demand, along with the 
corresponding impacts on pool-price prediction, or the expectation of dynamic 
loading patterns in telecommunications networks. The key lies in an 
appropriate {\em representation} of the data. The intended methodology is to 
extend the theoretical basis of the current state of the art on neural network 
feature extraction techniques to tackle real-world problems presented by 
industry and commerce. The emphasis is to seek appropriate representations 
of nonstationary data such that the resulting `clusterings' may be exploited 
to perform classification.  Because real data is generally nonstationary the 
principal axes of the feature space change in time and so we need to track 
this nonstationarity if `market' characteristics as determined by the features
are to be useful.

Potential candidates should be mathematically and computationally competent 
with a background either in artificial neural networks, dynamical systems
theory, statistical pattern processing, or have relevant experience from a 
physics or electrical engineering background. This post is tenable for three 
years.


        Neural Networks for Visualization of High Dimensional Data
        ----------------------------------------------------------

                               (One Post) 

Visualization has proven to be one of the most powerful ways to interpret and 
understand complex sets of data, such as records of financial transactions, 
corporate databases, customer profiles, and marketing surveys. Particular 
problems arise, however, when the data involves large numbers of variables, 
corresponding to spaces of high dimensionality. Additionally, the data is 
often plagued with deficiencies such as missing variables, mislabelled values, 
and inconsistencies in the representations of different quantities (for 
instance, the same attribute may be represented in different ways in different 
parts of the data base). Such problems severely limit the performance of 
current visualization algorithms. This project will investigate the 
theoretical basis for visualizing data using neural networks, and will develop 
practical techniques for visualization applicable to large-scale data sets. 
These techniques will be based, for example, on recent developments in 
latent-variable density estimation.

Potential candidates should be mathematically and computationally competent 
with a background either in artificial neural networks or a relevant field. 
This post is tenable for two years.


Neural Computing Research Group
-------------------------------

The Neural Computing Research Group currently comprises the
following academic staff

  Chris Bishop     Professor
  David Lowe       Professor
  David Bounds     Professor
  Geoffrey Hinton  Visiting Professor
  Richard Rohwer   Lecturer
  Alan Harget      Lecturer
  Ian Nabney       Lecturer
  David Saad       Lecturer (arrives 1 August)
  two further posts (currently being appointed)

together with the following Research Fellows

  Chris Williams   
  Shane Murnion
  Alan McLachlan   
  Huaihu Zhu        

a full-time software support assistant, and eleven postgraduate research 
students. 


Conditions of Service
---------------------

Salaries will be up to point 6 on the RA 1A scale, currently
15,556 UK pounds. These salary scales are currently under review,
and are subject to annual increments.


How to Apply
------------

If you wish to be considered for one of these positions, please 
send a full CV and publications list, together with the names of 4 
referees, to:

    Professor C M Bishop
    Neural Computing Research Group
    Department of Computer Science and Applied Mathematics
    Aston University
    Birmingham B4 7ET, U.K.
    Tel: 0121 359 3611 ext. 4270
    Fax: 0121 333 6215
    e-mail: c.m.bishop at aston.ac.uk

(email submission of postscript files is welcome)

    Closing date: 7 July 1995
    -------------------------