Postdoctoral Position: Applying Machine Learning to Ecosystem Modeling

[Tom Dietterich]

Postdoctoral Position: Applying Machine Learning to Ecosystem Modeling

Complex ecosystem models are calibrated by manually fitting them to
available data sets.  This is time-consuming, and it can result in
overfitting of the models to the data.  We are applying machine
learning methods to automate this calibration and thereby improve the
reliability and statistical validity of the resulting models.  Our
ecosystem model--MAPSS--predicts amounts and types of vegetation that
will grow under global warming climate scenarios.  An important goal
of global change research is to incorporate such vegetation models
into existing ocean-atmosphere physical models.  

Under NSF funding, we are seeking a Post-Doc to assume a major role in
carrying out this research.  Components of the research involve (a)
representing ecosystem models declaratively, (b) implementing
gradient and non-gradient search techniques for parameter fitting,
(c) implementing parallel algorithms for running and fitting the
ecosystem model, and (d) conducting basic research on issues of
combining prior knowledge with data to learn effectively.  The ideal
candidate will have a PhD in computer science or a closely related
discipline with experience in neural networks, simulated annealing
(and similar search procedures), knowledge representation, and
parallel computing.  The candidate must know or be eager to learn some
basic plant physiology and soil hydrology.  Computational resources
for this project include a 16-processor 1Gflop Meiko multicomputer and
a 128-processor CNAPS neurocomputer.

Applicants should send a CV, summary of research accomplishments,
sample papers, and 3 letters of reference to 

Thomas G. Dietterich
303 Dearborn Hall
Department of Computer Science
Oregon State University
Corvallis, OR 97331
tgd at cs.orst.edu

Principal investigators:  
Thomas G. Dietterich, Department of Computer Science
Ron Nielson, US Forest Service

OSU is an Affirmative Action/Equal Opportunity Employer and Complies
with Section 504 of the Rehabilitation Act of 1973.  OSU has a policy
of being responsive to the needs of dual-career couples.

Closing Date: July 5, 1994