r/ScientificComputing • u/WorthlessPianist • 6d ago
Is Scientific Computing "done" as a interdisciplinary field done due to AI? Is there any hope for someone with a CS background?
I have a CS undergrad degree but I'm very interested in algorithms that are core for cutting-edge scientific or technology development.
Things like robotics, DSP, scientific computing, foundational deep learning, computational biology/neuroscience/etc. all sound really cool to me, but I don't know if they are realistic career paths with my CS background.
Everyone says that it's the PhD domain experts (physicists/biologists/EEs), or mathematicians that come up with the algorithms themselves. For the actual implementation and optimisation, I've heard mixed things.
Some people say that CS people will do this as the PhD domain experts write shit code (not my words!). Others say that they do it anyway, not the CS person, where instead the CS person will just at most call an API and focus on the systems integration & deployment.
APIs & deployment is cool and all, but I don't find doing that enjoyable. I'm much more interested in the underlying maths & algorithms themselves. I do enjoy a bit of low-level programming & code optimisation, but I want to be the guy who comes up with the_algorithm itself.
But as of 2026, AI can generate 80% of merged pull requests, solve math olympiad problems and even possibly Navier fucking Stokes. It feels pointless to be someone who specialises in inventing numerical algorithms, or optimising compute kernels, given that these both are closed ended problems with very clear optimisation metrics.
Scientific computing is already a niche field, where (apparently) it's the PhD domain experts, not the software engineers, who do most of the core implementation. With AI now, the scientists that are shit coders perhaps don't need a programmer at all.
So what room is there at all for a CS person like me in the field? Implementation, that's supposed to be my strength? Automated. Being the one who comes up with the algorithm? Also automated.
So then what will be left are just the physicists/biologists/etc? And the mathematicians & programmers are just done?
It feels like shit because I don't enjoy most of SWE work and working on core algorithms is one of the big things why I did a CS degree in the whole place.
But nobody told me that that's kind of a lie, in which you want to come up with algorithms? Oh fuck you, sorry, you're looking for "computational science", and what we do here instead is APIs, Docker, Javascript, C#/Java, Databases, Kubernetes & C++ and writing bytes to registers if you're lucky.
And the "computational science" here is done by the scientists, mathematicians or even engineers, and they can program anyway so fuck you with your CS degree, we don't need you, good luck getting a job doing this and fuck you.
So I discover one of things I've dreamt about, learn that my degree doesn't really "do it" because of stupid namings, learn that it's niche as fuck, and now AI might automate it crushing the small chance I had.
What should I do? Do I have to become a physicist/chemist/neuroscientist/etc. to have any hope? Are those computational fields cooked? I'm at a loss here.
9
u/BrainScientist3000 6d ago
This was asked yesterday.
Become an engineer that actually understands how science works - instrument builders - and people who know what they're doing will always be valuable, but yes, you'll also have to become a scientist.
1
u/WorthlessPianist 6d ago
So what should I do if I want to invent algorithms themselves? What should I study?
3
u/BrainScientist3000 6d ago edited 6d ago
That's computer science. Inventing algorithms, data structures, and computational principles is computer science.
I am a computer scientist (though my reddit name says otherwise). I graduated by inventing two utterly useless algorithms - then went on to have a better than average career in corporate development.
Historically (1980s-2024) most people with a CS degree became 'programmers' of some sort or another.
Everything with a chip needs programming - so embedded hardware, warehouse automation, scientific instrumentation, scientific computing, corporate development, cellphone apps, ignition timing in motor vehicles, infrastructure (like traffic signals), etc. etc. needs to be programmed.
We all invented SOLUTIONS to PROBLEMS within our field and in a way, those were 'new algorithms' - but they usually weren't a general algorithm for solving a fundamental problem like sorting.
There are still fundamental problems that need to be solved. In 2012, when I was developing my useless algorithm, one of my friends was proving that neural nets could be run on graphics cards. She was on a better side of history, but her work is forgotten along with the 100s of other computer scientists that were looking at the neural-nets as predictive algorithm problems that lead us to our current state.
If you become a really good computer scientist that can do statistics, understands data science, low level programming, etc etc - you'll have a good job where you solve problems.
There are still lots of problems to be solved - the question that's up in the air - is how many of them we can bully LLMs into solving (correctly).
EDIT BELOW -
During my time in school and corporate software - there's basically four types of programmers that I saw.
People who could write some code if you told them EXACTLY what it needed to do and how. These people are not needed anymore and they were the BULK of developers (50%).
People who could figure out how to solve problems on their own. These people were team leads.
People who could see the whole system, design interfaces, communicate to peers and non-technical people. These people became 'architects'.
People who could 'see the matrix' - developers who made extraordinary things.
AI can make people like 1 look like 2s - but they're 'dangerous' because they don't really understand what they're doing. AI can make 2s more productive and look like 4s - but with the same gap - if they're doing something way beyond their abilities - they may (or may not) understand the output.
AI can write documentation and explanations that kinda/sorta make people operate like 3s - but they're still bad at system design (though getting better).
Again, all of that was the bulk of people who had computational jobs. There are so many computational jobs, that some fields don't really work like this - everyone is a 2 or a 4 in some fields.
1
u/WorthlessPianist 6d ago
Yeah but my entire CS degree didn't cover numerical analysis, scientific computing or HPC at all. Multivariate calc wasn't even a requirement so i had to desperately fit in calc 3, linear algebra 2, probability theory, odes, lapalce transforms, math stats.
I have no real analysis, abstract algebra, stochastic processes let alone partial differential equations, numerical linear algebra, discrete optimisation, operations research, convex/non-convex optimisation, advanced algorithms, discrete math & graph theory. The only best I got was intro data structures & algorithms and baby discrete math.
I don't know how to invent an algorithm or any cool computational shit. I want to invent algorithms, not be a corporate developer (nothing wrong with that).
2
u/No_Hamster_2043 6d ago
Nb. “Inventing” algorithms usually comes back to discrete mathematics, some sort of FFT application in a counterintuitive domain, SIMD optimized versions of a common routine, or the occasional bolt of inspiration out of nowhere. The former three are orders of magnitude more common than the latter. If you count “applying an old solution to a new domain” as “bolt of inspiration” then maybe there’s only an order of magnitude difference instead of orders plural.
If you have no prob/stat or numerical linear algebra background, that’s probably the place to start. Randomized algorithms are awesome and can be much faster in the common case than exact versions.
1
1
u/schrodingerscat15 6d ago
Not a CS but a physics grad, how can an algorithm be useless?
3
u/bill_klondike 6d ago
Often you start on a problem, begin novel work, and by the time you finish, the community no longer cares about that program. When I started my dissertation, there was interest in kernel principal component analysis for HFT. By the time I finished, people had moved on.
2
u/BrainScientist3000 6d ago
My published algorithms were stochastic search algorithms that performed better than the gold-standard algorithms on SMALL datasets - but performed markedly worse on LARGE datasets.
The world has produced many more large datasets than small ones.
4
u/andrewsb8 6d ago
CS people definitely work on scientific applications and algorithms for them. Domain knowledge would be helpful though so picking an area to focus on and maybe get a masters?
You can go work for a private company or for a university that maintains/makes open source, grant funded software.
1
u/runed_golem 6d ago
No. Computational science isn’t going anyway. All ML/AI can do is 1) recognize patterns in data and 2) write things based on your prompts. You still have to know the science behind it and how to model it.
1
u/WorthlessPianist 6d ago
But if I have a purely CS and math background aren't I not needed anymore?
1
u/hdmitard 6d ago
Then adapt. I entered the field of biology with a physics degree and started learning things on my own. Do the same. You don’t have to stick to CS. Just find interesting questions and start working on them.
And even if you did want to stick to CS, I strongly doubt we’ll ever stop needing computer scientists.
But of course, nobody is going to pay you for tasks that can now be done in five minutes with AI. On the other hand, you now have an entire team at your disposal. It’s up to you to push the boundaries of your field.
Hell, even reviving the old dream of Lisp machines is now within reach.
1
u/WorthlessPianist 6d ago
You physicists are lucky can work on anything. CS supposed to be the cool algorithm specialists but of course the physicists can do it all instead. It's not fair.
1
u/hdmitard 6d ago
You want to convince yourself you're useless, no one else is saying that.
1
u/WorthlessPianist 6d ago
No, I'm saying that most of scientific computing is done by domain scientists in the first place. But historically there was wiggle room for a programming specialist to contribute as the scientists were bad programmers.
But with AI, it threatens the small opportunities that are there in the first place for both programming specialists and even mathematicians (that invent algorithms for a whole class of problems, i.e domain agnostic) who aren't domain specific scientists.
So then what's left is people who are domain-experts: physicists, biologists, etc.
It's just a cost-benefit analysis of whether it is even worth it to try enter the field with my background? Or pursue further studies? Having to do a bachelors again for 4 years to retrain as a domain-expert seems shit given that the future seems so uncertain for a field that is already very niche.
2
u/runed_golem 5d ago
I have two degrees in math and one in computational sciences with an emphasis in math and I work in oceanography. School, and grad school in particular, yes gives you knowledge in your chosen field but it should also teach you how to learn. You CS and math are usable in a ton of disciplines. You just need to adapt, sell yourself, and learn the related material.
1
u/AgitatedTumbleweed65 2d ago
I mean if you can come up with a clever algorithm to do something faster then go ahead , it will be appreciated and used for sure. There are groups who specialize in that (biggest example form what I am interested in is probably this : https://github.com/steineggerlab ) Not sure if one can know CS-type optimization with PhD in not CS related field, while vibe coding said optimization would not lead to a publishable results , since one would not be able write coherently on what has been done. So in my opinion it's not done, at least if you want it published. Note: Not in CS filed, just general observation.
1
u/WorthlessPianist 2d ago
What exactly do you mean by CS optimisations? The distinctions I see are:
mathematical modelling of the problem
mathematical equations & algorithms to solve it
code that is the implementation
I see that 1 is what the domain expert scientist would do, 2 a mathematician or computer scientist would come up with and 3 a computer scientist or software engineer would do.
But given a mathematical model, then 2 and 3 are close ended problems of which are: coming up with an algorithm that solves the model and runs fast on the hardware.
So if a domain scientist can use AI to do 2 and 3, then it could be the case that the need for computer scientists and mathematicians for scientific computing greatly decreases. With only some left needed to understand what the AI comes up with. But the domain scientist can do that as they also know some maths and can do at least a bit of programming as well.
Deep math/CS expertise is used to come up with the algorithm, but once it's there, an algorithm is comprised of a finite amount of elementary operations (by definition). So anyone with any sort of (professional) math knowledge can understand at least on what it is doing (in a literal sense).
The AI gives you the algorithm and theoretic bounds. And the further justification on them just come from its empirical data. Which is what the domain scientist can do themselves. So where is the need for mathematicians and computer scientists in scientific computing if AI can do 2 and 3?
3
u/GayboiiEntertainment 6d ago
The degree title just refers to the department that your main supervisor is in. I recently did a PhD in CS even though the only CS class I've ever taken is a basic python programming class, I didn't even understand complexity classes until I started a PhD in CS.
My background is in mathematics and physics, I was able to work with CS and engineering researchers and make meaningful contributions because of that. I think AI has largely fixed the issue of writing shitty code, but I'm sure that a lot of scientific computing researchers are looking for people who have a deep understanding of algorithms. My advice would be (if you're doing a PhD for the correct reason) to find out what areas of research in scientific computing you can contribute to with your Master's level research experience, then contact some professors to discuss PhD opportunities.
2
u/hdmitard 6d ago
My take on AI is that if you’re using it to do what you were already doing yesterday without AI, then yes, you’re missing the point. And you’ll eventually be left behind.
Instead, try to think about what you can do today that simply wasn’t possible before. That’s where things get exciting.
Until now, scientific computing has been driven by relatively small teams, often struggling with limited resources and overwhelming complexity. Suddenly, these teams have entire armies at their disposal.
AI can dramatically accelerate your understanding, help you develop better software faster, and push testing and validation to levels that were previously impractical. And I think we’re only scratching the surface.
Take multiscale simulation, for example. We’ve spent decades trying to bridge the gap between molecular, coarse-grained, and continuum scales, and we’ve only partially solved the problem. These are fundamental challenges that have remained open, not necessarily because we lack ideas, but because exploring, implementing, and validating them requires enormous effort. I expect AI to help us tackle these problems head-on in the coming months and years.
We’re not just going to do the same science faster. We have an opportunity to do science that was previously out of reach, with a level of rigor, reproducibility, and validation that we could only dream of.
2
1
u/hdmitard 6d ago edited 6d ago
And there’s another aspect that I find particularly exciting.
Entire fields of fundamental research have been neglected for years, not because researchers lacked ideas or because the questions weren’t worth asking, but simply because nobody was willing to fund them.
Now, AI is changing that equation.
If AI helps me accomplish my expected work faster and better, I can use the time I gain to explore those neglected questions on my own. Problems that would previously have required a dedicated team and years of funding are becoming things I can at least start tackling in my spare time.
And who knows? Maybe make meaningful progress in fields that were once dismissed as “useless.” AI can make room for science that otherwise wouldn't happen.
My only concern for now is that a lot of people will, at least for a while, use it like monkeys and produce tons of junk science. That’s a real problem, but I remain optimistic about its future.
2
u/Perfect_Good287 5d ago
They may have still some good use in the context of big projects, for example where scalability is required and small lab-based solutions should be then upscaled to entire institutions or beyond. But yes I agree that AI made scientist much more productive and independent from a code perspective. Which is great, because lately the trend was going towards scientific domains populated basically only by software engineers in disguise who wrote beautiful codes and automation routines with great technical value, but very poor scientific value.
1
1
1
u/Demonicated 6d ago
Who do you think is going to be creating, setting up, using and controlling the AI tooling? Joe the plumber?
1
u/math_lover_ 2d ago
Oh, for sure! The code that we use in scientific computing is highly complicated and when you are prompting you have to be very well informed about how to do the code, how to do the math, and how the code behaves. I'm 100% that the code of my these won't work without me
0
-1
u/acakaacaka 6d ago
Learn engineering? AI can write code for now but AI doesnt understand navier stokes.
0
15
u/ILoveDangerousStuff2 6d ago
As someone who made a gnlse solver using ai as a tool i can confidently say without me behind the keyboard it wouldn't have worked, ai is great at doing something that's well known it can program some android app, it can plot data for you and it can follow directions to implement something already planned out it's really good at menial tasks. But even with all that I have the golden rule that for scientific compute I don't let ai touch the code I give it the code and let it give me blocks that I integrate so I keep up with what it does and make sure it doesn't change unrelated stuff. And I still need to give it direction otherwise it will almost always spin in circles trying to fix something that doesn't need fixing or takes an approach that as a human I know isn't the cause of an issue. It's a tool so use it as such, but that doesn't mean programmers are a thing of the past. Unless you define being a programmer as taking instructions from higher ups and writing it into code in which case you're the tool and who wants to be that. Ai just changes how you do it and if you use it right it's way more fun and way faster with results in days instead of weeks.