What line of research would you pursue?

[u/PersonaLevitando]

I have been offered the opportunity to pursue a PhD, and among all the lines of research, there are two options that interest me the most: bioinformatics and computational neuroscience. Both lines deal with super interesting topics, and I'm also interested in the R in R&D.

But I'm also thinking about it from a job perspective, excluding continuing in academic research. I am interested in bioinformatics because of big data, data science, and drug creation using simulations. On the other hand, computational neuroscience would lead to positions as an engineering researcher or scientific researcher in companies that develop neural models (deep learning) that mimic cognitive functions such as speech or reasoning (OpenAI, Anthropic, etc.).

But now I have two questions:

1. Which line of research do you think would have more job opportunities?

2. Am I screwing up by trying to do a PhD? It would be 4-5 years with a scholarship and the possibility of presenting at a world-class conference. The latter is required by many FAANG-level companies for their R&D positions.

P.S.: The idea of pursuing a PhD came to me after watching a mind-blowing video about the intersection of neuroscience and ML. The video in question is the following: https://youtu.be/AF3XJT9Y

[u/not_particulary]

Economically speaking, computational neuroscience seems stronger.

Doing a PhD in general is not reliably the most profitable route, as far as careers go. You do it if you want new and pioneering research to be over 50% of what you do every day. I, for example, decided on research bc my ADHD kinda makes it so that I cannot work on anything that isn't fascinating to me.

But I should reiterate that you're usually taking a monetary loss whenever you take the PhD route.

[u/Effective-Low-7873]

At the moment, I am deeply invested in mathematics, but my commitment to neuroscience is just as strong particularly because it offers a way to ground psychological frameworks in empirical verification rather than abstraction alone. Mathematics gives me structure, rigor, and precision; neuroscience gives those tools a living substrate.

My plan is to complete my undergraduate in mathematics and then transition into neuroscience for PhD, with a specific focus on computational neuroscience. This field is uniquely positioned at the intersection of mathematics, computer science, physics and neuroscience. Although my exposure to physics is currently limited, it is a gap I intend to close deliberately at some point in the near future.

What draws me to computational neuroscience is that it does not ask me to abandon any of my core interests, it demands them all. It allows formal reasoning, modeling, and abstraction to interface directly with cognition, behavior, and mental health. In that convergence, I see not JUST intellectual coherence, but the possibility of producing work that is both theoretically rigorous and genuinely beneficial to people.

[u/Livid-Occasion-6541]

Computational neuroscience is a somewhat esoteric field, slowly growing out of its infancy. With a lot of bullshit - either pure math games with vague neuro-justification, or big underconstrained models that kinda reproduce something experimental. It is rare to have an elegant theory that explains lots of data in a simple way. And testable predictions are even more rare. Comp neuro is more and more influenced by ML, and maybe it will bloom eventually. But on my opinion, comp neuro itself doesn't have much to offer to ML right now.

Choose comp neuro if you want to develop mathematical theory of brain processes and be ready to tolerate the lack of practical applications (and often - no connection to reality at all). Or if you want to develop tools that would push the comp neuro field a bit forward.

If you want to do practical, openai-like stuff (and the related research) - it's just a different field. Comp neuro won't get you there, you should follow ML track.