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Develop state-of-the-art computational methodologies for the analysis
and interpretation of large-scale gene expression datasets generated
by DNS micro-array experiments.  Analysis of genome-wide expression
patterns and their correlations with phenotypes that may provide
insights into the structure of genetic networks and into biological
processes not yet understood at the molecular level.  Apply supervised
and unsupervised learning to a variety of classification and
exploratory data analysis problems in biomedicine.  Assist in
development of Java and Perl scripts to implement algorithms and
methodologies.

For examples of Genome Center research, please review: "Molecular
Classification of Cancer: Class Prediction by Gene Expression
Monitoring," Golub et al, Science, October 15, 1999: 531-537;
"Interpreting Gene Expression with Self-Organizing Maps: Methods and
Application to Hematopoeitic Differentiation," Tamayo et al, PNAS 96,
1999: 2907- 2912; and "Dissecting the Regulatory Circuitry of a
Eukaryotic Genome," Hostege et al, Cell, 95, 1998: 717-728.

Required qualifications include: PhD in Machine Learning, Statistics
or Computer/Physical Sciences; postdoctoral level research experience
in pattern recognition/KDD; and excellent analytical, data analysis,
and Java or Perl code development skills; background in
bioinformatics, biology or medicine is a plus.

Please send your resume to: Ellen Snowdon, Human Resources/oks,
Whitehead Institute for Biomedical Research, 9 Cambridge Center,
Cambridge, MA 02142, or e-mail to: snowdon@wi.mit.edu. For additional
information about Whitehead and the Genome Research Center, please
visit our web site at: www.wi.mit.edu. The Whitehead Institute is an
equal opportunity/affirmative action employer.

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