Sorry if this isn’t the right place for this question but I couldn’t think of anywhere better to put it.
So I finished my degree in computer science a couple years ago right when the tech crash just started hitting, and the job market has been an enormous clusterfuck. Instead of trying to get a job where everyone seems to be going all-in on LLMs, machine learning, and crypto bullshit, I’d really like to be able to put my programming skills to good use helping out scientific research in some way, but I have no clue where to start. While in college I did help out my university’s biology research department by writing small programs here and there to help undergrad/grad students who weren’t very knowledgeable about technical solutions, but because of the recent funding cuts to scientific research and education, everyone there is struggling harder than I am.
Ideally I’d love to help contribute to causes that help improve people’s lives (or astronomy just because space is cool). Does anyone know of resources I could look into to start down this path?
Lots in biology research, since biologists tend not to be good coders. That being said, the requirements for biology are rather interdisciplinary and a serious position will likely require you to also have advanced biological knowledge. Based on my impressions, you’ll basically be playing biologist for 50% of the time and programmer for the other 50%.
Second this, and I also agree that this comes with a lot of caveat…
Biology as a field has an issue with looking down on anyone without a PhD and sometimes people can get weird over it; there are also LLMs and machine learning bullshit (I’ve dealt with some personally); and frankly the most in-demand skill is bioinformatics, not traditional CS… but yeah it is not a bad field
Personally though… I might be giving bad advice here, but I find some bioinformatics tools rather poorly maintained. This is FastQC which is one of the more important tools in bioinformatics data processing, and… yeah its GitHub records look like that, most are way less maintained. I always wonder if some of these projects could use some help with maintenance
Medical research comes to my mind. Endless amounts of knowlege about the human body are still waiting to be discovered.
if you want a guaranteed job, where you want… learn how to program the medical charting apps.
EPIC is a major player and as someone who spent over 20 years in medicine IT, let me tell you a good EPIC programmer/developer is worth their weight in gold. They get a cushy roll and they all seem to have a good time. Bonus is they can’t afford to lose you once you have a grasp of their systems.
I work in IT in healthcare at an epic shop, and everyone I’ve ever talked to that has either worked at or knows someone who works at epic says it’s a meat grinder. They seem to like young staff that they can work long hours for several years, so may want to ask around to see if their work culture is for you.
Also, if I was looking for rewarding programming work, an EMR would not be where I’d look for many reasons.
FWIW, I know several developers at Epic who are happy with the job, the work/life balance, and have been there for years. OTOH, I know several people, too, who were project managers, and that’s 110% true. Epic is big on academic performance. It wants people who can put their heads down and grind, without asking questions or sticking up for themselves.
Until they burn out…
Medical charting apps is a very good suggestion. EPIC is not a good suggestion - they’re not a good choice for their customers. Contribute to alternatives to EPIC instead.
I would love some examples of how they aren’t a good choice for their customers.
Drawbacks of EPIC
Super expensive - only large outfits can even afford it.
Poor design - a multitude of modules that often use different design principles so knowing one module doesn’t help much with another.
Extreme vendor lock-in - EPIC is very similar in business model to Microsoft in the first decades, basically a mafia.
Lack of interoperability - EPIC interfaces poorly with lab and diagnostic equipment, EPIC actively fights development and adoption of interoperabilty standards.
Dictating Clinical Workflow - EPIC is designed primarily to assist billing, not record keeping for patient benefit. Thus workflows are highly constrained and significant time must be spent clicking about to get the system to let one do normal things.
I mean, EHR is inherently complex so any EHR, but EPIC makes it much worse than it needs to be.
Twenty years ago, I briefly worked for a research group doing genomics stuff. The researchers couldn’t code worth shit, so they had a hard time analyzing results in a reasonable amount of time. It was easy to be a hero.
I suspect new researchers would be way better coders (I assume AI may help too).
The pay was shit.
About those genomics stuff… most biomedical researchers still couldn’t code worth shit. It is bad enough that there are even dedicated computational biology programs now (I was in one), and from personal experience I confirm that even comp bio graduates can barely code worth shit and are also somewhat bad with biology
Pay is still shit. So… yeah.
That’s shitty on a bunch of levels. Is there a bright side? Are there jobs out there?
Is there a bright side?
To be fair I might have exaggerated a bit… I can navigate my way around pretty advanced statistics/machine learning stuff, it’s just that I don’t have enough fundamentals to call myself a programmer; I assume most of my classmates are similar. But on the positive end, there are a lot more advanced methods in biomedical research now. People used a lot of cutting-edge machine learning in biomedical research (case in point: IBM and DeepMind had biomedicine in mind when they are trying to diss chess champions with AI models). Also there are some very competent programmers/research groups who ended up building open source bioinformatics tools that everyone could use, even though it seems against the hyper-competitive trend of biomedical research. So even if individual labs couldn’t do much, there are indeed better tools/pipelines available now
Are there jobs out there?
I… think a lot of research labs/pharmas are still pretty desperate for competent (or just any) bioinformaticians? Not in computational biology/methods development though, that field is too competitive even for me (and there are a surprisingly large amount of AI/ML/LLM slop)
Nope, most researchers are still poor coders. Coding is a skill that takes time to learn if you even have someone who can check your shit (very rare). “Vibe coding”/using AI for research analytics is probably done and it is also probably shit.
Pay is still shit.
Pay is still shit.
Tracks
One place to look is 80000hours.org.
One interesting science field is “discrete AI” (probably has a few other names) which basically technically means “based on integers instead of floating point numbers”. It has a few more implications on the models being more mathematically clean, but that’s a long paragraph if I get into it.
The expecations are AI that is not based on absurd computing resources and black boxes, but getting the same benefits from low-power low-cost hardware and with outputs that can be more realistically queried to explain why the output became what it was.
E.g. if AI is used to make decisions on when to feed fish, and it feeds slightly too much, you’d want to be able to ask “why” and get a useful answer instead of today’s “yeah idunno magic computer said so i guess training data lol”
“discrete AI” (probably has a few other names)
- symbolic AI
- traditional AI
- GOFAI (good old fashioned) AI
Kinda sounds like you’re talking about Explainable AI too. Very interesting set of fields, but I’m pretty sure they’re all having funding problems too.
Yeah, funding is kinda not. I assumed the question was ignoring that, but I may have been mistaken.
Tsetlin machines are the ones I found most interesting. Strict yes/no logic stuff in the actual decision model, while the deeper complexity is in the training.
Sounds interesting. Glad those topics are still being investigated. So important to remember that even those neural methods labored for decades in the shadows before they finally found the answers they needed.
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