Postdoc in hybrid modeling and/or analysis of live fuel moisture content and wildfire

The Remote Sensing Ecohydrology Group at Stanford University, led by Prof. Alex Konings is looking for a postdoc to research live fuel moisture content dynamics in the context of Western US wildfires. The postdoc could work in one of multiple areas:

a) hybrid biogeophysical-machine learning-based modeling efforts for plant hydraulics/LFMC and other vegetation variations in the context of wildfire 
b) developing new microwave remote sensing datasets of live fuel moisture content using machine learning
and/or
c) analyzing the interaction been live fuel moisture content and wildfire and ecological dynamics

If you have expertise in machine learning (especially but not necessarily hybrid physical-machine learning models), wildfire, large-scale plant hydraulics, remote sensing of vegetation water,  you may be a great fit for this position. To apply, please send a cover email and CV to Alex Konings at konings@stanford.edu. In your cover email, please explain what aspects of the position you are most interested in and why.  The pay is commensurate with experience, and is adjusted for the high cost of living in the Palo Alto, CA area where Stanford is located. University regulations preclude remote positions. The appointment length will be for up to 24 months with the potential for extension.