3 ms·

Sigh. This gets posted every few months and the title is still wrong. It's a great time to be a scientist, especially if you're into, say, computational biology

Sigh. This gets posted every few months and the title is still wrong. It's a great time to be a scientist, especially if you're into, say, computational biology. It's an awful time to shoot for a tenure track faculty position. There are lots of ways to do science, both in and out of academia, without the 'professor' title.

mbreese 12y ago
Sigh, it isn't a good time to be a scientist. Even if you are into computational biology (which I am). The "science" is amazing, and we can learn so much more with less than we could even when I started grad school a bit over ten years ago. However, there just aren't the jobs available for the number of people that are being trained. I'm somewhat lucky that I was a programmer first, molecular biologist second, so computational work was a no-brainer. However, it is still extremely difficult to find jobs past the post-doc stage, and that stage does last about 5 years longer than it used to. And post-docs get paid like crap for each of those years. There are lots of career options available, but really, academia is still the place where science gets done. I know lots of people work in industry, startups, biotechs, pharma, etc. But unless you're developing a drug, you're probably trying to sell to academia. There are some great projects and good work being done, but with the lull in Federal funding, the long-tail of grant funding isn't there to support existing labs, let alone new ones. Debating the reasons for all of this could go on for hours... and there are lots of reasons and enough blame to go around. It's a great time for science, but a bad time to be a scientist. It was never lucrative, but it just isn't a viable profession for many.
tjradcliffe 12y ago
Yes. Do be a scientist. Don't be an academic. Dropping out of academia was amongst the more difficult decisions I've made. Also amongst the best. There are huge opportunities for scientists today. I'm a physicist. I've worked in computer-assisted surgery, medical physics, image processing, computer vision, genomics, computational physics and a few other things besides. It has paid well and let me do all kinds of interesting research that I would never have been able to touch as an academic. Things to do as a student: study modern statistics hard. Being a card-carrying Bayesian is like the secret handshake to an unlimited world of really cool stuff these days. There's a childhood friend of mine who's (finally) finishing his PhD in psychology, and when we reconnected a few years ago we were amazed that we could meaningfully talk to each other about our work because we both spoke the same statistical language. It really is a universal grammar, and anyone in any field who speaks it fluently has a world of opportunity waiting for them, from finance to fisheries.
- Xcelerate 12y ago
  > study modern statistics hard This is something I've been thinking about doing. My work is in computational materials science, and I often feel there is a complete lack of statistical rigor in analyzing the results. (Peter Gill joked at a conference that the only difference between applied mathematicians and quantum chemists is that the mathematicians calculate the error once they get their answers). I know basic statistics: distributions, standard error, correlations, etc. But I don't know anything about the terms Bayesian or Frequentist. In your opinion, what would studying these topics provide for me?
- noobermin 12y ago
  I'm curious, how far into your academic career were you when you dropped out? Post-doc? Grad student?
- wavegeek 12y ago
  > study modern statistics hard Would you recommend some texts that cover this? A friend suggested: * Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling * Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern * Introduction to Statistical Inference by Jack C. Kiefer, Gary Lorden Currently I am working my way through this. Pattern Recognition and Machine Learning by Christopher M. Bishop These books are expensive and very time-consuming so it is important to pick the right ones.

mbreese 12y ago

Sigh, it isn't a good time to be a scientist. Even if you are into computational biology (which I am). The "science" is amazing, and we can learn so much more with less than we could even when I started grad school a bit over ten years ago. However, there just aren't the jobs available for the number of people that are being trained. I'm somewhat lucky that I was a programmer first, molecular biologist second, so computational work was a no-brainer. However, it is still extremely difficult to find jobs past the post-doc stage, and that stage does last about 5 years longer than it used to. And post-docs get paid like crap for each of those years. There are lots of career options available, but really, academia is still the place where science gets done. I know lots of people work in industry, startups, biotechs, pharma, etc. But unless you're developing a drug, you're probably trying to sell to academia. There are some great projects and good work being done, but with the lull in Federal funding, the long-tail of grant funding isn't there to support existing labs, let alone new ones. Debating the reasons for all of this could go on for hours... and there are lots of reasons and enough blame to go around. It's a great time for science, but a bad time to be a scientist. It was never lucrative, but it just isn't a viable profession for many.

tjradcliffe 12y ago

Yes. Do be a scientist. Don't be an academic. Dropping out of academia was amongst the more difficult decisions I've made. Also amongst the best. There are huge opportunities for scientists today. I'm a physicist. I've worked in computer-assisted surgery, medical physics, image processing, computer vision, genomics, computational physics and a few other things besides. It has paid well and let me do all kinds of interesting research that I would never have been able to touch as an academic. Things to do as a student: study modern statistics hard. Being a card-carrying Bayesian is like the secret handshake to an unlimited world of really cool stuff these days. There's a childhood friend of mine who's (finally) finishing his PhD in psychology, and when we reconnected a few years ago we were amazed that we could meaningfully talk to each other about our work because we both spoke the same statistical language. It really is a universal grammar, and anyone in any field who speaks it fluently has a world of opportunity waiting for them, from finance to fisheries.

Xcelerate 12y ago
> study modern statistics hard This is something I've been thinking about doing. My work is in computational materials science, and I often feel there is a complete lack of statistical rigor in analyzing the results. (Peter Gill joked at a conference that the only difference between applied mathematicians and quantum chemists is that the mathematicians calculate the error once they get their answers). I know basic statistics: distributions, standard error, correlations, etc. But I don't know anything about the terms Bayesian or Frequentist. In your opinion, what would studying these topics provide for me?
noobermin 12y ago
I'm curious, how far into your academic career were you when you dropped out? Post-doc? Grad student?
wavegeek 12y ago
> study modern statistics hard Would you recommend some texts that cover this? A friend suggested: * Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling * Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern * Introduction to Statistical Inference by Jack C. Kiefer, Gary Lorden Currently I am working my way through this. Pattern Recognition and Machine Learning by Christopher M. Bishop These books are expensive and very time-consuming so it is important to pick the right ones.