This post is from Phil Price.  I work in the Environmental Energy Technologies Division at Lawrence Berkeley National Laboratory, and I am looking for a postdoc who knows substantially more than I do about time-series modeling; in practice this probably means someone whose dissertation work involved that sort of thing.  The work involves developing models to predict and/or forecast the time-dependent energy use in buildings, given historical data and some covariates such as outdoor temperature.  Simple regression approaches (e.g. using time-of-week indicator variables, plus outdoor temperature) work fine for a lot of things, but we still have a variety of problems.  To give one example, sometimes building behavior changes — due to retrofits, or a change in occupant behavior — so that a single model won’t fit well over a long time period. We want to recognize these changes automatically .  We have many other issues besides: heteroskedasticity, need for good uncertainty estimates, ability to partially pool information from different buildings, and so on.  Some knowledge of engineering, physics, or related fields would be a plus, but really I just need someone who knows about ARIMA and ARCH and all that jazz and is willing to learn the rest. If you’re interested, apply through the LBNL website.