Finally I can announce here that (modulo the uncertain visa process) I will soon be moving even further from my astronomy roots, taking up a geostatistical/epidemiological modelling position in Zoology at Oxford University. Some of my friends and colleagues have asked me, “how can you do zoology, don’t you do astronomy?”, or “what do you know about epidemiology? I thought you were in statistics?”. The short answer is simply that, yes, zoology wasn’t the subject of my PhD thesis, but underpinning all these fields are three common threads: (1) the scientific method, (2) mathematics, and (3) scientific computing. In principle, any postdoc from astronomy could transition quickly to zoology, and any zoology postdoc could transition quickly to astronomy (ditto with statistics). The reasons it doesn’t happen more often are the same as why most astronomers (again ditto for zoology & statistics) never venture away from the narrow focus of their PhD research: (a) the academic career system rewards repetitive research / penalises diverse research, and (b) a lot of astronomers simply have no desire to learn new skills (math, computing, etc.) after finishing their PhD. I could easily go on a long riff here about what’s wrong with academia (like how come so many mediocre scientists end up in the precious few permanent astronomy jobs, while their more talented peers *cough cough, my friend, RHK* are scattered to the diaspora), but instead I’ll sign off for today with some good advice for anyone thinking about changing fields or simply broadening the focus of their research.
It’s bloody easy to learn a new skill (how to do Bayesian model selection, how to code a simple N-body orbit integrator, how to simulate from a compartmental epidemiological model) or concept (what’s Lebesgue integration? what’s this BIC all about?) if you have the motivation to do so — simply pick up a relevant book (or download one from your university library / the Chinese/Russian interwebs) and read it in your spare time. You’ll be surprised: a lot of what your colleagues do is a lot easier to understand than they want to you think!