Postdoctoral Fellowship in Bayesian Biosurveillance

The ISDS has received the following job opening to pass along to our membership. Contact information for the position is listed at the bottom of the entry.

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The Cooper Laboratory in the Department of Biomedical Informatics (DBMI) at the University of Pittsburgh invites applications for a two-year postdoctoral fellowship involving research on Bayesian modeling and inference for disease outbreak detection and characterization. The position is available starting December 1, 2009. The project is advancing the state-of-the-art in research at the intersection of computer science, epidemiology, Bayesian methods, and graphical models. It is an integral part of DBMI's new CDC Center of Excellence in Public Health Informatics, which is being led by the RODS Laboratory.

The project specifically involves developing, implementing, and evaluating new Bayesian computational methods for detecting outbreaks of disease as soon as possible from healthcare data, such as emergency department records. It has access to large sets of data that are relevant to disease outbreak detection and characterization. The project team includes public health officials, who are responsible for detecting disease outbreaks in the population, and a key goal of the project is to provide them with state-of-the-art Bayesian methods for detecting and characterizing outbreaks in the service of improving public health. The research project involves a tight loop between theory and practice, with the goal of advancing both.

The postdoctoral fellow will be involved in all aspects of the project, particularly in bridging between the conceptual aspects of the research (in collaboration with the project faculty) and the implementation of those concepts as computer programs (in collaboration with the project programmer). The fellow will also be centrally involved in evaluating the methods that are developed.

Candidates must have a Ph.D. in biomedical informatics, computer science, machine learning, statistics, or a related field. Candidates must also have a strong working knowledge of the Java, C++, or a related programming language. Knowledge of Bayesian statistics and Bayesian networks are desirable, but not required.

Three letters of recommendation should accompany the application or be sent independently at the same time to the contact below.
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Please send a curriculum vitae and the letters of recommendation to:

Ms. Daphne Henry
e-mail (preferred): dahst44@pitt.edu
fax: (412) 802-6803
phone: (412) 648-6738