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PhD student position in the study of networks, Albuquerque NM (U.S.A.)

Ph.D. Research Assistantships, starting in Fall 2009, are available in the 
study of social, biological, and technological networks in the Computer 
Science Department at the University of New Mexico.

Cristopher Moore's research group uses both rigorous mathematical 
techniques and data-driven techniques from machine learning, statistical 
inference, and statistical physics to study networks.  A few sample 
publications:

A. Clauset, C. Moore, and M. E. J. Newman, ``Hierarchical structure and 
the prediction of missing links in networks.'' Nature, 2008.

D. Achlioptas, A. Clauset, D. Kempe, and C. Moore, "On the bias of 
traceroute sampling: or, power-law degree distributions in regular 
graphs.'' Journal of the ACM, to appear; conference version in STOC.

C. Moore, G. Ghoshal, and M. E. J. Newman, "Exact solutions for models of 
evolving networks with addition and deletion of nodes.'' Physical Review 
E, 2006.

We work closely with Aaron Clauset (Santa Fe Institute) and Mark Newman 
(University of Michigan, Ann Arbor), as well as many other collaborators. 
Our work is funded by the National Science Foundation and the McDonnell 
Foundation.

The University of New Mexico's Computer Science department prides itself 
on interdisciplinary research.  Other faculty relevant to our group 
include Tom Hayes (randomized algorithms and Markov chains), Jared Saia 
(distributed computing), Shuang Luan (approximation algorithms), Terran 
Lane (machine learning), and Melanie Moses (scaling in networks).

If you would like to know more about our group, please contact Cris Moore 
at moore@cs.unm.edu.