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Comment by daemonk | Hacker News Reader

I am graduating phd bioinformatician, most likely going to transition into industry. It's very easy to be caught up with the self importance of academia because you are essentially in a bubble. It's great to be passionate about science, but I really dislike religifying academia. It's almost expected of aspiring academics to live like monks and just to be okay with shitty pay and long hours. That's bullshit and academics take it while constantly assuring themselves that "it's important and they love it". I am sorry that I am coming off as extremely cynical, but I really don't think propagating the idea that pursuing pure science is somehow more virtuous than other professions helps with the situation.

And in my opinion, as inexperienced as it might be compared to more established scientists, computational biologists are ready for biology, but biologists are not ready for computational biology.

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There is definitely an academic bubble. The infamous ivory tower. But in biology specifically, many of the problems are objectively important (as judged by society). And some people really do love spending all their waking hours working on them, and don't give a crap about the money.

I wouldn't call it virtuous, but it is deeply intellectually satisfying.

Agree about biologists not being ready - computational biology needs more computer scientists, not more biologists.

Glad to see someone's enjoying it. I think I came out my program more jaded than the average student.

I agree that there are intellectually satisfying problems to solve. However, without getting into the tedious debate on the values of basic science vs translational science, how much of that intellectual satisfaction is mental masturbation?

Are these problems really that important? How much of the cool intellectual questions will directly give you a meaningful biological interpretation? Perhaps this is more of a comment on our field. I found a lot of the intellectual satisfying questions during my phd to involve algorithms/data structures, which mostly are just the tools to get at the biological interpretation.

One bubble which could use piercing is the hard science one (please, humor me).

Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so growing more food won't necessarily help).

IMHO, the important problems in the world are much more social and political, than technological. The work twitter does may easily have greater beneficial impact, direct or indirect (Arab spring and all that), on global food security than working for Monsanto on GE crops. I wouldn't be so self-righteous for choosing to work on "hard" science problems. Are you really doing it for the benefit of the world, or just for the deep satisfaction of your own curiosity?

It's too bad you got downvoted. Your first question was pretty good.

That's an excellent point. Food insecurity is caused by a wealth of factors including social, logistic and agricultural. In addition to the problems you highlighted, were are currently approaching the maximum yield capacity for many crops, and are pushing the maximum land under cultivation for some.

There are huge problems to solve in all those areas. The biological problems are made more important by the lack of progress in solving the world equality problems. By 2050, when the world population is something in the region of 9-12 billion, either billions will be starving or we will have solved one or more of those problems. The science problems are tractable, while the others are ill defined and involve many factors we cannot control, so I think there's a stronger moral imperative to work on the science.

The other consideration is that working in a job that, by chance, invokes positive social results is not equivalent to working directly on trying to solve a problem. Progress in science suffers because there aren't enough good people working on these problems, because so many of them are seduced by industry.

I don't work for Monsanto; that's a straw man. We're talking about academic computational biology jobs.

The answer to your last question is: both. I couldn't do a job where I didn't satisfy my curiosity. But I know working in tech would do that just fine - there are hard problems in many fields. I chose science because I want to use whatever skills I have to try to solve the problems I see.

I completely agree. I work in plant sciences (as a bioinformatician) but my background is in econ, pol sci, and stats. Unless things have changed since I switched fields, most global food security problems are market related. There's more than enough food, and we're really good at getting it all around the globe quickly. Also, huge amounts of food are lost due to post-harvest losses. I'd bet a beer that in terms of absolute food weight to mouths, post-harvest research may have higher benefit/cost ratio than photosynthesis research. Evolution has been pretty damn good at getting photosynthesis as efficient as possible (read R. Ford Denison's Book Darwinian Agriculture for this point argued well).

Having experience in both fields, I don't work in plant genomics because I want to feed more people (I do, but if that's what solely motivated me I'd be working under economics still). I do it because genetics is awesome, and plants are great to study.

But, I'd argue that the hard sciences are always a good worth investing in. Being capable of trying to understand our world with the scientific method is something that is uniquely human. We should use this talent as much as possible. Drosophila (fruit fly) genetics is a great example. Decades ago, drosophila was chosen because it was cheap to grow in a lab and had a short generation time. Yet through drosophila we've learned so much about genetics, development, and evolution in ways that are just unparalleled. Yet Sarah Palin[1] and others attack it as a silly waste of money. If we'd have limited drosophila research decades ago because we didn't think an organism with a ~700 million year split with humans would be useful for us, where would be? Much stupider, and much worse off. Basic science matters, big time.

[1]: http://www.youtube.com/watch?v=HCXqKEs68Xk

Evolution has been good at getting photosynthesis as efficient as possible in the most extreme cases. In rice and all other C3 plants, it could be at least 50% more efficient in the majority of field situations. That's what we work on. Projected yield improvements are on the order of 50%.

Absolutely agree about the importance of post-harvest problem-solving, but I disagree about the benefit/cost ratio. There are a few key things in photosynthesis which, if achieved (which won't cost that much), could have massive benefits. In post-harvest research there are many small, localised problems that change over time. It's a less tractable, but extremely important, set of problems.

> shitty pay

I can see this complaint in humanities academia, but pay in the sciences past the PhD student level is pretty reasonable. You could probably make more elsewhere, but it's not like you're scraping by on ramen noodles as a bioinformatics professor or anything. Postdocs typically make $50-60k, and professors start at something like $90k at the minimum, easily up to $120k, $150k, or more after tenure, especially if you're in a hot area like bioinformatics, have made a name for yourself, and can get a position at a top-30ish place. Unlike in tech, those salaries often come in places with a lower cost of living than SF, too (at least if you want them to). Six figures goes pretty far in Atlanta, Austin, Urbana-Champaign, Ames, or Raleigh, for example.

You could beat that in industry, but either way you're making solidly in the top 10% of U.S. salaries. And if you really need more money, most universities will let you do 20% consulting time, or do a spinoff startup. There are admittedly other reasons not to go into science academia (the list is pretty long, actually), but fear that you'll have to take a vow of poverty doesn't seem like a strong one.

My biochem friend just accepted a post-doc at a respected lab for $39k.

For just wet-lab people, this is still a little low, but not by much.

Postdocs in biological/physical sciences are easily 3-6 years for < $60k (generally closer to $40-50k in the past 5 years), and the chances of getting an academic position with meaningful salary are very low. Postdocs are plentiful, positions are scarce.

Meanwhile the postdoc could have been making $100-120k for a profession where the job market is almost the polar opposite, which makes a difference for job security/stress. The 2x-4x salary difference, especially during the late 20s and early 30s, is a pretty big deal, especially if you're able to save that extra 2x-4x.

The even crappier pay in humanities makes it basically undoable except for the upper class or outsizedly talented. The low pay in science makes it doable for normal people, but there is a strong financial incentive not to.

NIH trainee pay scale: http://grants.nih.gov/grants/guide/notice-files/NOT-OD-12-03...

Depending on the institution, you may make slightly more than the minimum (starting at $39K), but postdocs in the sciences do not 'typically' make $50-$60K. As a monetary investment, academia is about as poor a bet as you can make: spend 5-6 years making ~$25K then another 3-5 years below $50K. Then you might be able to start making professor money if you're in hot field and willing to sell your soul to your work.

Interesting, that's lower than the people I know. They lean more towards the machine-learning side of bioinformatics (plus people in straight AI, not bio-related), and generally make around $50k, some $55k, at research institutions in the US.

Sounds like recent American graduates may want to start reading the job listings in Europe, though. A postdoc where I teach in Denmark has a minimum civil-service salary of ~$55k, and in Switzerland the going rate is well above that: a friend works in Lugano, fresh out of grad school, for somewhere in the neighborhood of $80k, although that's a bit above the norm. Postdoc candidates with strong tech skills have good demand at institutions with large EU projects, so they're not lottery-win positions either.

This response tells me you've never been in the science trenches. I've never heard of a postdoc making anywhere close to 60k, even at elite schools in high COL areas. The financial opportunity cost is staggering.

Well, you'd be incorrect, since I'm currently a science academic. Have you checked what Stanford, or Georgia Tech, or UT-Austin pay postdocs with machine-learning or data-mining experience, in the past 5 years? There are definitely areas that pay less, but bioinformatics, if by that you mean people with serious computational skills, pays above the norm.

Getting a postdoc at Stanford has the same probability as playing in the NBA. Only you get paid $60K instead of $60M.

If you have strong machine-learning experience and a few good publications in the current market, getting a postdoc at a top institution is nowhere near NBA odds. I don't know what the odds are specifically for Stanford, but if you apply to whoever has openings among top schools, there are many each year. If you know something about biology and a lot about machine learning, labs might even recruit you rather than vice versa.

Also, finishing a post-doc from a top lab does not mean you will get a professor job at a decent school.

This has certainly been my experience as a recent PhD in computational biology. I pretty much have my pick of Post Doc positions - I was getting offers before I even graduated. I was also able to negotiate 50k without much trouble, but I'm definitely worried about the opportunity cost. Giving up 100+ k for more than a year or two seems like a poor decision.

Also a Post Doc from a top lab directly correlates with how much $$ you can make in industry.

Depends on what you do...

Straight wetlab postdocs are usually around the NIH levels (~40K). For computational postdocs (especially if you have a good biology background), 50-60K isn't out of the norm.

Those salary expectations only hold in the very top tier of universities.

From [1]: Median starting salaries for assistant professors are more like $75k. Median full professors--who are nearly 50 years old--are earning $120k. (Admittedly this does not control for field.)

That's about what a green PhD gets offered at age 28 for a data science job in SF.

[1] https://chronicle.com/article/aaup-survey-data-2013/138309#i...

I spent so long at uni they started paying me to stay - not quite got a PhD yet so I'm still kicking around the system, but unless you're very lucky uni is a pretty crappy place to work. A lot of what used to be good about working at uni has been stripped away by increasing class sizes, increasing bureaucracy and decrease in discretionary time.

A couple of years ago I got a long term freelance gig which I was describing to a professor I talk to about once a year the other day. He said:

"Interesting work, good money, and they leave you alone to get on with it. Sounds brilliant".

Although my current work has next to nothing to do with what I did at uni, it was a valuable experience. However I don't think most people are as lucky as me.

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