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.
Another day, another stats postdoc