Project Title  Complex System Modeler 
Summary  The position is to generate a hybrid mathematical and agentbased model for modeling disease spread through a population. The concept is to model an experimental population with published properties. 
Job Description  First, 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 complexsystem properties emerge. Experimental data suggests the population must be at least a few thousand individuals in size. 
Conditions/Qualifications  Must be a postqualexam PhD student with knowledge of C or C++ programming. 
Start Date  06/01/2016 
End Date  05/30/2017 
Location  UIUC, Beckman Institute, Urbana, IL. Department of Bioengineering, Advisor: Michael Insana, mfi@illinois.edu 
Interns  Sahand Hariri Akbari
