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Position TitleComplex System Modeler
SummaryThe position is to generate a hybrid mathematical and agent-based model for modeling disease spread through a population. The concept is to model an experimental population with published properties.
Job DescriptionFirst, it is important to understand current models and their limitations. Then adopt a fairly standard population dynamics model (e.g., SIR) to represent individuals. Model parameters need to be random. These individuals will interact using an agent based model, were rules of interaction are defined and adaptable. The measureable output needed will be determined depending on the preliminary results, however the output will be related to histograms of population variables measured on an adjustable scale in space and time. The algorithm needs to be scalable since no one knows how large the population needs to be before complex-system properties emerge. Experimental data suggests the population must be at least a few thousand individuals in size.
Conditions/QualificationsMust be a post-qual-exam PhD student with knowledge of C or C++ programming.
Start Date06/01/2016
End Date05/30/2017
LocationUIUC, Beckman Institute, Urbana, IL. Department of Bioengineering, Advisor: Michael Insana, mfi@illinois.edu
Interns
Sahand Hariri Akbari