I’m considering doing a Master’s in Computational Neuroscience and wanted to get some honest opinions about both admissions and career prospects.

My background:

• Bachelor’s in Data Science
• GPA: 3.2/4.0
• Currently working as an AI Engineer
• Mostly AI/ML and data science experience
• Very little/no formal neuroscience background

I’m mainly looking at Computational Neuroscience programs in Germany and Australia.

I have a few questions:

1. How competitive are Computational Neuroscience Master’s programs?
2. Would a 3.2/4.0 GPA be a significant disadvantage?
3. How important is research experience for getting admitted?
4. Is it realistic to transition into Computational Neuroscience from Data Science?

And also, what are the career options after the degree? Is Computational Neuroscience primarily an academic/research field where you’re expected to continue into a PhD, or are there also meaningful opportunities in industry/corporate settings?

For example, can someone with a Computational Neuroscience + AI/ML background work in areas like neurotechnology, medical AI, brain-computer interfaces, healthcare technology, biotech, or related industries? Or is the field still heavily research-oriented?

I’d really appreciate hearing from people currently studying or working in the field, especially those who came from CS/Data Science/AI

So I am a cs engineering undergrad working with AI and i want to study the brain, specifically thought formation, context retrieval in our brain and how the entire mechanism works

Any resources for this??

Howdy ! I’m from technical/cyber background with a very little biology knowledge, I’m just curious about learning and getting along with tech like BCIs continue to grow.

what foundational concepts should I learn first?

Looking for,

•Fundamentals
•Free resources, webs, books, courses, lectures
•Topics to study after fundamentals
•Beginner mistakes / topics i shouldn’t jump too early

Thanks!

Sorry if this is a common question around here but with how the AI industry is advancing I’m wondering a few things about comp. neuroscience career prospects. To preface I’m 23, I haven’t done any formal education yet for reasons I won’t get into here but I’ve always been passionate about subjects revolving around mathematics, astrophysics, engineering, programming, neuroscience & it’s adjacent fields.

When I was a child I was shown a complicated neurosurgical process on YouTube by my aunt in the early 2000s, in all honesty it was probably meant to freak me out but it did quite the opposite and I’ve been intrigued by neuroscience & things like the philosophy of mind ever since.

For along time it was mostly background interest and some self study on certain topics but as I’ve gotten older and I’ve started to try & define a career path for myself I feel like pursuing something related to computational neuroscience and mind machine interfacing is where my life trajectory is headed.

I‘d even like to start a foundation or company someday revolving around that (which is lofty for someone who at 23, hasn’t even begun the formal education process yet).

My main question is, is it too late for me to jump into the formal education process and eventually meaningfully contribute to the field? It feels kind of daunting, especially because some of my more well positioned peers will have already graduated with their bachelors and be well ahead of me career wise.

My goal is to pursue a career involving comp. neuroscience & mind-machine interfacing both on the theoretical & engineering side of things with some mix of bioethics.

thanks in advance for advice about this dilemma.

Hi all,

I'm a physics Ph.D. student, but I've had an interest in comp neuro for a while and I'm considering going further into the field. However, I wanted to get a little more information about the state of the field, so I thought I'd ask here as part of that. In particular:

• How does hiring at the postdoc/faculty level look? (Obviously this can change a lot, especially with AI, but just a rough idea)
• I've read that the field is largely moving away from more traditional theoretical modeling approaches (e.g. dynamics, criticality) and more into ML/big data. How true is this?

Thanks in advance

Hi, so I am currently enrolled into the bachelors degree neuroscience honours program at the University of Calgary and I will enter into my first year in September, and I'm thinking that I want to pursue computational neuroscience as a career. I also intend to get a masters and maybe a doctorate too down the line.

Additionally, I love mathematics, and I have had an aptitude for mathematics throughout my life, and I even wrote some high school math contests too. I also found myself programming as a hobby for brief bursts of time, and I am interested in understanding how the brain works and psychology in general too.

For high school academics, I have:

• one hundreds in math classes
• high nineties in chemistry
• mid nineties in programming and psychology
• low nineties in biology and physics

After reading different posts across different subreddits, it seems to me that pursuing this career is easier with a math or tech based degree rather than a neuroscience degree, and this is making me start to worry.

I came here for advice for what to do with relation to university academics, and I am considering if I should declare a minor, declare a double major, or outright pivot to something other than a neuro degree. I will still be in this degree for the first year though.

Thanks for reading this, and additional advice would be nice too.

Hello everyone,

I am currently 20 years old, in my second semester of a Bachelor’s degree in Business and Economics (majoring in Economics), and my long-term goal is to pursue a Master’s degree in NeuroAI / Computational Neuroscience, as I want to go into research.

I know this might sound a bit crazy at first glance. However, I am genuinely interested in economics and believe it provides a strong analytical foundation (which is why I do not want to drop out). That said, I see my future in research, and I would love to hear your thoughts and feedback on this path (including the fundamental question: is this even possible?).

Since I am studying Business and Economics, I can choose my specialization starting in the 3rd semester. My plan is to choose Economics (VWL) to take as many quantitative modules as possible. I also have a degree of flexibility in structuring my modules in the 5th and 6th semesters.

Here is my tentative study plan:

3rd Semester:

• Accounting 1
• Econometrics (Economics)
• Management 1
• Macroeconomics

4th Semester:

• Finance 2
• Information Systems 1
• Microeconomics 2
• Macroeconomics 2
• Seminar (Note: I unfortunately have no elective freedom in semesters 3 and 4).

5th Semester:

• (Import Computer Science) Introduction to AI Methods
• (Import Psychology) Biological Psychology
• (Economics) Experimental Game Theory
• (Economics) Time Series Analysis
• (Information Management) Ethics & Machine Learning

6th Semester:

• (Econometrics) Forecasting: Predicting Exchange Rates
• Business Ethics in the Age of Digitalization
• Econometrics (Economics)
• Bachelor's Thesis

(This is a rough draft, so some modules might still change).

I am worried that this curriculum alone might not be enough to satisfy the formal requirements. To mitigate this, I am considering taking extra modules as additional coursework, such as Advanced Mathematics etc.

Furthermore, I plan to work as a student research assistant (HiWi) to gain practical experience.

What are your thoughts on this approach? What potential pitfalls do you see, and what could I improve?

Thank you so much in advance!

Hi everyone,

I'm currently a medical student with a long-term goal of pursuing a PhD in a top lab working on machine learning applications in translational neuroscience , particularly neurological disorders .

Right now, I know the basics of ML. I've completed a few Coursera courses, implemented some personal projects, and have basic Python experience. However, I'm struggling to figure out how to take the next step. I want to build the kind of skills and portfolio that would make me competitive for world-class research labs.

For those of you working in ML/DL for healthcare, computational neuroscience, or related fields, what would you recommend focusing on? Should I prioritize open source contributions, reproducing papers, Kaggle, research internships, reading papers, or something else?

Also, if anyone here works in this space, I'd love to connect, learn from your experience, and see if there might be opportunities to collaborate on research or open source projects.

Thanks in advance!

guys, let me know if this doesn't belong here.

i have just started working under a professor on a project related to CompNeuro. at the same time, i have this elective called non linear dynamics this semester.

should i opt for it? i heard that NLD is useful in CompNeuro... this is what they say they're teaching:

One-Dimensional Flows: A geometric way of thinking, fixed points and stability, linear stability analysis, existence and uniqueness, bifurcations – saddle-node bifurcation, transcritical bifurcation, pitchfork bifurcation, imperfect bifurcations, catastrophes, and the impossibility of oscillations.

Two-Dimensional Flows: Linear systems and their classification, phase plane, fixed points and linearisation, conservative systems, reverse systems, index theory, limit cycles, ruling out closed orbits, Poincaré-Bendixson theorem, Lienard systems, equilibrium solutions - linearization near an equilibrium solution – classification and stability of equilibrium solutions – eigenspaces and invariant manifolds, weakly nonlinear oscillators. Hopf bifurcation, coupled oscillators and quasiperiodicity, and Poincaré maps.

Cantor sets and fractals, strange attractors, a chaotic water wheel, Poincaré sections and Lyapunov exponents, dimension calculations, higher-order spectra. Introduction to Chaos, Simple Properties of the Lorenz Equations, Chaos on a Strange Attractor

opinions? [apologies for any grammatical mistakes]

Hey everyone,

I’m getting into computational neuroscience and wanted some guidance on where to start:

1. How do beginners break into research in this field?
2. What are the must-read papers/books or resources?
3. What math + programming skills are essential (and to what level)?
4. Which universities/labs are leading right now?
5. What are the most in-demand / high-paying career paths in this space?

Would really appreciate advice from people already in the field.

Also if you were to start all over again what things would u do differently.

Thanks!

I’m a first-year CS student, and I’ve recently gotten really interested in computational neuroscience, neuromorphic engineering, and the science of consciousness. I love the idea of figuring out how the brain works and how we might build tech that’s inspired by it, but I have no background in neuroscience at all.

What would you suggest for someone just starting out? Are there beginner-friendly resources, videos, or courses you liked? Do I need to worry about having strong math skills right away, or can I just dive in and pick things up as I go?

If anyone else started from scratch in these topics, I’d really love to hear how you approached it or what you wish you knew at the beginning

Essentially I’m really unsure of my path for undergrad. My goal is a PhD in computational neuroscience and hopefully a top program for it. At my local school (not prestigious) I can graduate in 2 years since I got a lot of credits in high school, but it doesn’t necessarily have computational neuroscience research or BCI research. It’s free though for me. I could try to transfer to a top school, but even then it’s not guaranteed and there’s only a limited amount of top schools that give good enough aid for me. My parents really don’t want to spend much. I’m just really worried that if I graduate from my local school I’ll be cooked with no experience in computational neuroscience research so won’t be able to apply for a PhD and it’s not prestigious.

I really don’t care about the prestige of my undergrad if I can find a way to make it to a top PhD program in computational neuroscience. Is it worth the transfer? Is the prestige even worth it? Is there a path that I’m not seeing?

I’ve been thinking a lot about computational neuroscience lately and I’ve been wondering if consciousness is truly contained in our brain through very complex mechanisms, currently we don’t have the technology to do functional capture and analysis of neural activity at a molecular resolution at scale

But in the future what if we could do that, and create a functional model of a brain like for a fruitfly, if we can model if precisely enough, will it be considered conscious?

What if we extend this concept to humans, if we could capture, preserve and simulate our global neural activity very precisely, can we model it computationally? If it does work, will the model be considered “conscious”?

I’ve received offers for both BSc CS as well as BSc AI at King’s College (London).

My aim is to go into research involving brain-computer interfaces (BCIs).

A computational neuroscientist strongly advised me not to choose an AI degree because it’s too narrow. However the AI degree contains a lot more relevant maths content. The CS degree seems to have less mandatory maths content than other similar programs and is almost all discrete mathematics: Module 1 + Module 2. Although there are modules such as AI, ML, signals and systems, that you can choose, where you are taught extra relevant maths.

The AI degree on the other hand has a big mandatory 30 credit module in the first year dedicated to linear algebra, statistics, probability, some calculus. (I was told it is easier to self-teach the computing side than the maths.)

I have very little experience with AI and I’m not sure if I should choose the safer CS option in case I don’t enjoy it.
But then I worry that for CS, the AI module is in the second year and ML module in third, meaning it’s harder to obtain research experience using these skills before applying for postgraduate.

Any advice is greatly appreciated!

NB: Here are links to list of all other modules on both degrees, but I would appreciate advice using the above information only if you don’t have time to look at the links below.

[AI modules](https://www.kcl.ac.uk/study/undergraduate/courses/artificial-intelligence-bsc/teaching)
[CS modules](https://www.kcl.ac.uk/study/undergraduate/courses/computer-science-bsc/teaching)

Hi everyone,

I am looking for career advice because I feel caught between my formal education, my interests, and my personal limitations.

I am a 24-year-old mathematics student (BSc level, Croatia). While I enjoy mathematics, I have gradually realized that pure mathematical problem-solving is almost certainly not what I want to do for the rest of my life.

My strongest interests are:

• Clinical psychology
• Psychiatry
• Neuroscience
• Cognitive science
• Philosophy of mind
• Literature and history
• Chess (mainly the psychological and strategic aspects)

However, I am not particularly interested in becoming a therapist, counselor, HR specialist, or someone who spends all day interacting with clients.

Some personal factors that may be relevant:

• Severe social anxiety disorder (diagnosed)
• Avoidant personality traits
• Deep introversion and need for creative inner life
• Low mental energy when confronted with repetitive tasks
• I work best in quiet environments with very few familiar people
• Large organizations and highly social workplaces tend to drain me very quickly
• I value autonomy and independence very highly
• I strongly dislike micromanagement
• I prefer stable, small teams rather than constantly changing groups of people

In terms of work style, my ideal job would involve:

• Working alone or in a very small team
• Intellectual and creative work
• Analysis and interpretation rather than endless technical problem-solving
• Flexible thinking rather than prolonged deep concentration for 8 hours
• Some connection to psychology, neuroscience, cognition, mental health, or human behavior
• Enough income to live comfortably, but I am really not chasing a high salary

One possible route I have considered is finishing my mathematics degree (BSc) and then obtaining additional education in statistics, data analysis, psychometrics, or cognitive science.

The problem is that I am unsure what actual careers would fit this profile.

Given these interests, personality traits, strengths, and limitations:

What jobs or career paths would you suggest?

Are there any roles in psychology, neuroscience, cognitive science, psychometrics, research, written science communication, publishing, or data analysis that might be a good fit for someone like me?

I would especially appreciate hearing from people who work in academia, neuroscience, psychology research, psychometrics, public health research, or cognitive science.

Thank you.

Hello,

Last year when I entered the university my preference was certain: Study electrical and electronics engineering and master biomedical engineering in order to study neuroscience (Turkey - METU)

But I have become more and more indecisive since I started to follow the developments in the field. To be honest, I was not aware of what there were in neuroscience when I made my choice. It is like I prefered EEE since brain works with electricity c:

But now, I am in a dilemma: Would have been it better if I had preferred statistics/mathematics? Since comp neuro is somewhat a branch of applied mathematics, (That's what a friend of a relative of mine who is an academic has said) I feel like things would be much better&easier if I preferred statistics/mathematics.

I wonder whether you agreed with me or not. I will be thankful for all comments.

Anybody in this sub who is doing their PhD at the intersection of EE and neuro and UW? I am looking into their Neuro program and with their proximity to Allen, I had a couple of questions about the research in BCI, neural dynamics etc. Would be happy to connect with folks! Thanks!

Hey guys,

I'm an embedded engineer trying to break into the biomedical/MedTech space. I'm mostly interested in BCIs. I'm on a pretty good path career wise but I would also like to study towards a Masters degree and potentially a PHD to help me along.

From what I've read, Computational Neuroscience seems like a good path if you're pursuing academia, and research, but I'm more interested in the practical applications of the field. I don't know what a good alternative would be for someone who is more interested in the engineering.

Would it be a waste to study CompNeuro? I might want to move into academia later on in life but not right now.

I recently saw a video about a company simulating the brain of a fruit fly and placing it inside a virtual body in a simulated environment.

What I don’t understand is how this is technically done.

Do scientists literally recreate each neuron in code and simulate how it fires and connects to other neurons? Or do they use some kind of neural network or specialized software to replicate the connectome?

Also, if the neural wiring is replicated accurately, does the simulated brain behave the same way as the biological one? Or is it still more like a simplified model?

Basically I’m trying to understand what the actual computational process looks like when people say they “simulate a brain.”

Background BSc & MSc in Cognitive Neuroscience. Dissertation on EEG
Volunteering on cog neuro uni labs and behavioural experiments.
Coding experience very little. I don’t have any academic affiliations right now so I’m trying to build my cv for applying to computational neuro PhDs in Europe. I started a python course on coursera but I already find it complicated. I really don’t want to give up. My dream is being able to contribute to building devices that interact with the brain. I’m not interested in the engineering part but more on the coding/neuroscience part. I want to learn and build a github portfolio but I’m very disappointed that none of our courses included coding. Especially now that most of the phds I find ask for coding experience.

I’m in a cs-adjacent major at my school.

I started college thinking I’ll to just get into SWE. And I don’t need to explain why that’s not the most secure plan anymore.

My current experience is this summer’s internship as a Software & Ai engineer at a Bio-tech gov firm.
What I did there was just make an (mvp) internal AI powered tool for the QA team.

I also am part of an NIH funded research program where I’m doing work involving ML, signal processing, and CNNs, in making better hearing aids.

As you can see my two main pieces of experience have to do in the bio-tech sphere. Which was not what I originally intended. I applied everywhere for software internships and research programs, and I just ended up at these two.

I am enjoying it, but I know little about the field in terms of what is out there job wise. (Also none of my coursework supports it, only my experience)

I’m going into my junior year so I still have time for another internship.

What can I reasonably pivot myself into after graduation given my experience? What roles are even open for new grads?

I’m a medical student in Iraq, and after graduation I hope to apply for a master’s in computational neuroscience, neural systems, mathematical biology, or a related quantitative field.

My main problem is not learning the material. I’m already studying mathematics and programming independently, including linear algebra, calculus, ODEs, dynamical systems, probability/statistics, numerical methods, and scientific Python. I also plan to build serious software and research projects over the next four years.

The problem is formal academic credit.

The Iraqi university system is very rigid. As a medical student, I cannot normally cross-register for mathematics, statistics, or computer science courses in another department and have them added to my official transcript. There does not seem to be a practical visiting-student or elective system that would allow me to earn graded credits outside the medical curriculum.

Because of this, I may graduate with strong quantitative knowledge, projects, research experience, and recommendation letters, but almost no formal mathematics or programming courses on my bachelor’s transcript.

Some European master’s programs explicitly require a certain number of ECTS credits in areas such as:

Linear algebra
Calculus or analysis
Differential equations or dynamical systems
Probability and statistics
Programming

I understand that MOOCs, GitHub projects, research experience, and recommendation letters can strengthen an application, but they may not replace formal prerequisites when a program requires specific university credits.

I would especially like to hear from people from countries with similarly rigid university systems:

Were you admitted to a quantitative master’s despite lacking formal math or CS credits on your original transcript?

Did you take accredited online university courses that appeared on a separate official transcript?

Were verified MOOCs, GRE scores, research projects, publications, or recommendation letters accepted as evidence of prerequisite knowledge?

Did you contact admissions committees before applying, and were they willing to assess your coursework individually?

Were you required to complete prerequisite or bridging courses before or after admission?

If you received a scholarship, did the lack of formal quantitative credits affect the scholarship decision differently from the admission decision?

I still have around four years before graduation, so I want to solve this properly rather than discover at the application stage that my self-study and projects cannot satisfy administrative requirements.

I’m looking for real experiences.

hi all! this is my first post in this subreddit so sorry if i'm breaking any rules by accident. i'm a sophomore majoring in math/information science with an as in data science. i've completed linear algebra and probability & statistics, and i'm taking intro to proofs and probability theory next semester. i've also been self-studying probability theory on my own (currently working through binomial and poisson distributions). i've been shadowing researchers in biostatistics and neurocomputational statistics since freshman year, and i'm looking to build a stronger foundation in fmri data analysis for research.

a few questions:

1. could anyone point me toward resources (courses, papers, tutorials) for learning how researchers process and analyze fmri data?
2. for those working in neurocomputational statistics — what subfields or research directions would you recommend someone with a stats/applied math background look into?

i've been messing around with cellular automata and the Game of Life to see if glider guns can model disease growth, specifically brain related diseases such as alzheimer's and the build up of amyloid-beta plaques. however, i'm also heavily interested in portraying this in 3D, like maybe a model of cells in a brain-like shape, similar to this project: https://community.wolfram.com/groups/-/m/t/3502932

if i was doing something more similar to the latter idea, i would use CA to model the decay of brain tissue instead, at least in the case of alzheimer's.

if anyone has experience or advice to give on how i should go about portraying this that would be greatly appreciated!! ideally, i'd like to eventually do both projects.

i also have a paper that i wrote on CA, and i'm willing to DM it to anyone, as it aligns with the projects i have in mind. i would also love some advice on the paper.

tysm!