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Postdoctoral Research Associate position at Duke University
: Atmospheric chemical transport, Bayesian modeling & inverse methods :
 
A post-doctoral research position is available at Duke University in
projects on inverse modeling using the GEOS-Chem global chemical transport 
model to advance and improve inference on global reactive trace gas fire 
emissions using remote sensing measurements. 

The project will focus on  developing and applying advanced statistical 
modeling and estimation techniques (Bayesian computational methods such as 
particle filtering, ensemble Kalman filtering, etc.) in spatial time series 
for integration of large-scale remote sensing data with predictions from 
atmospheric transport simulation models. The project is headed by 
Dr. Prasad Kasibhatla in environmental science and policy, and involves 
collaboration with Mike West and others in Statistical Science. 

Qualifications include a PhD and proven research abilities in complex 
modeling and scientific computation.  We particularly seek applicants with 
backgrounds in atmospheric sciences and a sound conceptual understanding 
of tropospheric oxidant chemistry, a good background in mathematics and 
statistics, and experience in large-scale atmospheric chemistry modeling. 
Strong applicants from statistics and allied backgrounds with relevant 
experience in environmental applications are also encouraged. 

Appointment is available immediately. 

For further information, please contact Dr. Prasad Kasibhatla (psk9@duke.edu)
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