Postdoc position at Bellcore

[Joshua Alspector]

              POSTDOCTORAL POSITION IN NEURAL NET RESEARCH

The Adaptive Systems Research Group at Bellcore, Morristown, NJ is looking for
a postdoctoral researcher for a period of 1 - 2 years starting approximately
November, 1989.  Bellcore has a stimulating research environment for neural
computation with active programs in neural network theory and analysis, in
applications such as speech recognition and expert systems, and in optical and
electronic implementation.  This is an excellent opportunity for a researcher
to be exposed to this environment while contributing to the effort in VLSI
implementation.

A test chip that implements a neural network based learning algorithm related
to the Boltzmann machine has been designed, fabricated and tested.  The next
step is to implement a useful, multiple-chip system that can learn to solve
difficult artificial intelligence problems.  We will extend our study of
electronic implementation issues to large scale systems using a three-pronged
approach:
   1) Further development of learning algorithms and architectures suitable for
   modular VLSI implementation.  To be useful, algorithms must be implementable
   because learning by example takes too long using serial computer
   simulations.  Therefore, the algorithms should take into account the
   constraints imposed by VLSI.
   2) Functional simulation of large scale hardware systems using benchmark
   test problems.  We will build a computer-based development system for
   testing algorithms in software.  This will be composed of software modules,
   some of which eventually will be replaced by hardware learning modules.  A
   computation module may be run on a remote parallel machine.  This will serve
   as a platform for algorithm development, will perform functional simulation
   of a hardware system before design, and also will be the front end for
   testing the chips and boards after fabrication.
   3) Design and fabrication of prototype chips suitable for inclusion in such
   systems. As a first step in the development of large scale, modular VLSI
   systems, our learning test chip will be expanded to contain more neurons and
   synapses and to enable construction of a multichip system.  This system
   would be taken to board-level design and fabrication.  Evaluation will
   involve a speed comparison using a variety of benchmarks of three neural
   network implementations: software on a serial machine, software on a general
   purpose parallel machine, and special purpose neural hardware using the
   board level system we build. Chip and board design will be carried out using
   a combination of sophisticated VLSI CAD tools.

The successful candidate should be involved in many aspects of this work
including the design of algorithms and architectures for VLSI neural
implementation, computer programming to simulate and test the existing and
proposed neural architectures, and the design of analog and digital chips to
implement them. He or she should be capable of doing independent publishable
research in neural network learning theory, in parallel software simulation, in
applications of neural information processing, or in VLSI implementations of
neural network learning models.

Please enclose a resume, a copy of a recent paper, and the names, addresses,
and phone numbers of three references.  Send applications to:

 Joshua Alspector
 Bellcore, MRE 2E-378
 445 South St.
 Morristown, NJ 07960-1910