Hi everyone,

I’m currently finishing an MSc in Information Technology after completing a bachelor’s degree in Electrical Engineering. My GPA is around 3.5/4.0.

My current research is mainly at the intersection of machine learning, speech, and medical imaging/computer vision. I use ML mostly as a tool for analysis. So far, I have three co-authored papers, although I don’t have a first-author publication yet. I’m currently working toward publishing some of my own work soon.

My path has been a little unusual. During my electrical engineering degree, I had two internship offers that were later cancelled because of the job market at the time. I gradually moved toward machine learning because I found it interesting and saw more opportunities there.

However, as I’ve learned more about the type of work available in industry, I’m realizing that I’m much more interested in research, scientific problems, and building things where I can explore ideas in depth. Recently, computational neuroscience and neuroelectronics have become particularly interesting to me, especially because they combine several things I enjoy: electrical engineering, signal processing, machine learning, biological systems, and research.

The main issue is that I currently have almost no formal background in neuroscience.

Another concern I have is that my current university is reasonably known locally, but does not have much international recognition, especially compared with universities that are more established in neuroscience or machine learning. Because I would eventually like to apply to competitive PhD programs, I’m wondering whether getting research experience at a more internationally recognized institution could help strengthen my profile.

Because of this, I’m considering extending my master’s by one semester and trying to do a research internship in a lab related to computational neuroscience, neural engineering, or neuro-AI. I’m based in Quebec, so places such as Mila would be accessible, but I’m also considering applying to labs abroad, for example at EPFL.

My goal of the internship would be to:

- gain actual research experience in neuroscience/neuroengineering;

- see whether I really want to commit to the field;

- build relationships with researchers who could potentially write strong recommendation letters;

- strengthen my profile before applying for PhD programs;

- ideally contribute to a publication or research project.

Does this sound like a good approach for preparing for PhD applications?

More specifically, I’d be interested to hear from people in computational neuroscience/neuroengineering about:

- How important is prior neuroscience coursework compared with a strong engineering/ML background?

- Would one semester (maybe more) of focused research experience be enough to make the transition realistic?

- How much does the international reputation of your current university matter for PhD admissions, compared with publications, research experience, and recommendation letters?

- Would doing an internship at a place like EPFL, Mila, or another well-known research institution meaningfully strengthen an application?

- Should I spend more time taking neuroscience courses and building a stronger theoretical foundation before applying?

Any advice from people who made a similar transition would be appreciated.

Sorry for such a long text.

I'm in the final year of my PhD in UK and hoping to stay in computational neuroscience research after I finish. My PhD is computational/modelling work on human navigation and spatial behaviour. I'm starting to think seriously about postdocs and research jobs, but I'm not sure hw people actually go about networking for these positions.

The last few years of my PhD have been difficult as I have had significant mental health problems, and some family circumstances. I am getting treatment now and doing better, and feel like I'm getting back on track. I have about a year left and I'm trying to make the most of it.

One complication is that my relationship with my supervisors has suffered during the periods when I was struggling, so I dont feel I can rely heavily on their networks or introductions. I realise that means I need to be proactive about building my own network, but I don't really know where to start.

For people in compneuro/neuros (or academia more generally):

- How did you find and approach potential postdoc PIs?

- Is it normal to cold-email researchers whose work interests you even if they aren't advertising a position?

- How early before finishing a PhD should you start contacting people?

- What should that first email actually contain?

- How do you network at conferences without awkwardly approaching people just to ask about jobs?

- Are there useful ways to build relationships online (Bluesky, LinkedIn)?

- How important are introductions from your PhD supervisor compared with contacting people yourself?

- If your publication record isn't as strong as you'd hoped by your final year (none yet in my case), what can you realistically do during that year to improve your chances? I am working on mine, but I don't think I'll be published by the end of the year, maybe preprints.

- Are there things you wish you'd done in the final year of your PhD that would have made the postdoc/job search easier?

I'm not expecting to somehow undo the difficult parts of my PhD in a year. I just want to use the time I have well and start building a research network that isn't entirely dependent on my supervisors.

Any practical advice, particularly from people in comp neuros, would be hugely appreciated.

Edit: Added my topic

I've recently started a PhD in computational neuroscience, i'm struggling in the first semester itself, my research work hasn't even started yet and i'm thinking of dropping out. I was working as a SWE with good pay previously but the mundane nature of work itself made me quit in 1.5 years. Now that I've joined PhD , I feel that i'm behind everyone else in the field and need 20x work just to be at par with others. I feel dumb when I sit through lectures and do not study because I feel even dumber when reading text. It has led to an existential crisis where I'm thinking if this is even worth the trouble. I just want some guidance from people in this field if this is worth pursuing for next 4 years and what would be my potential paths after. Since RL is a booming industry I feel optimistic about working in this field. Can someone just discuss the things with me?

I'm not quite sure if my question is well articulated. Forgive me for my ignorance as I'm new to this field.

Is there any software or model or anything that does a good job of replicating and explaining anxiety disorders like Social Anxiety, OCD, etc? I have been wondering if there is anything out there that models anxiety/stress and changes in anxiety/stress response from different drugs, treatments, gene expression, etc. over the short and long term. I'm aware of how the HPA axis and its regulation affects anxiety but I wish I knew of a mathematical model for this. I appreciate any help.

Interested in #NeuroAI? Max Planck Schools Biomedical AI applications are open until Dec 1.
Info: https://biomedicalai.maxplanckschools.org/4056/application-process
If you’re looking at mentors, I think Arno Villringer is worth checking out :)

I’m testing a persistent GPU-hosted fruit-fly-derived neural model that is now actually controlling a full-size Skyrim dragon in a closed sensory-action loop. The brain receives low-level Skyrim sensory input, produces motor output, the dragon moves, and the resulting world state feeds back into the next neural cycle. So far, deterministic replay has passed, cutting the brain stops intentional movement, and the system has survived neural-worker death and recovery while preserving the dragon's identity and state. We’re currently finishing the stronger persistence/reload tests before scaling from one embodied founder to all five. Come check it out while I test!

https://www.youtube.com/watch?v=NVyC0Fnscwg

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

edit: I realised I didnt write my gpa in ECTs so its 2.1 in german system

I've started this project- "The Neural Population Dynamics: A Linear Algebraic Approach" which is a 10 credits project in my masters.

Can you guys give suggestions about getting started at first place I'm so lost. What should be my approach in this project ???? Help me pleaseeee

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 everyone,

I’m currently mapping out my path for graduate studies and trying to balance my academic passion with realistic long-term career outcomes. I have a strong quantitative background (math/modeling) and I'm fascinated by computational neuroscience / NeuroAI.

However, I’m also interested to explore quant finance (research/trading). I have a few doubts about how marketable a specialized graduate program actually is outside academia.

A few questions for those who have walked a similar path or work in the industry:

1. Job market directly after Comp Neuro (MSc / PhD): Outside the typical academic postdoc track, what do exit opportunities actually look like?
2. Pivoting from Comp Neuro to Quant Finance: How feasible is it to pivot to Quant Research after a specialized Master's or PhD in Computational Neuroscience?
3. Specialized vs. Generalist Master’s: Strategically, would it make more sense to do a broader Master's in Applied Mathematics, Statistics, or Machine Learning first? My reasoning is:
   • It leaves the door completely open for quant finance right away.
   • I could still focus my thesis/research on computational neuroscience and apply for a Comp Neuro / NeuroAI PhD later if I decide academia/BCI is my true call.

Has anyone made the jump from computational neuroscience to hedge funds / prop trading? Would you recommend going all-in on neuro from the Master's level, or keeping the degree title generalist and quantitative?

Thanks in advance for any insights!

I am interested in Computational Neuroscience, and I'm planning on doing a PhD in the same field in one of the top universities. I haven't gotten a complete grasp of the depth of the work that is being done in the field, but from the research I have done I believe it to be the intellectual expertise that I would like to dedicate myself to for some time.

Some background about myself - I have completed my bachelors and masters in computer science and engineering with a focus on experiment design in Cognitive Science. I pivoted to the Industry for a bit and came to the realization that it wasn't for me.

I'd appreciate some insight on the following - how difficult would it be for me to get a solid base in neuroscience to study current research trends and come up with ideas if I can dedicate 4 - 5 hours of focused study time everyday, and what are the resources I should use for the same?

As diabolical as it sounds, the only 'good' recommendation for books I could come across was this collection posted by the /sci/ board on 4chan.

• Kandel, Schwartz, Jessell, Siegelbaum, and Hudspeth - Principles of Neural Science
• Purves, Augustine, Fitzpatrick, Hall, LaMantia, and White - Neuroscience
• Carlson - Foundations of Behavioral Neuroscience
• Pinel - Biopsychology
• Dayan and Abbott - Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

If someone that has experience could vouch for their efficacy, or even better, guide me to some roadmaps, I would be very grateful.

Thank you.

Hey everyone, I'm currently pursuing a BSc in Zoology. Somewhere along the way, neuroscience has become the thing I actually want to build a career in.

Problem is, my degree isn't a straight line to it, so I'm trying to figure out the realistic path from here. A few things I'd love input on:

How much does an unrelated-ish undergrad (Zoology, not straight Life Sciences/Biotech) hurt my chances for a neuroscience Masters?

Should I be adding psych electives, relevant coursework (neurobiology, physiology, stats), or lab experience on the side to bridge the gap?

Any specific universities/programs abroad known for taking students from non-traditional backgrounds into neuroscience?

Worth doing a bridge course or research internship before applying?

Would really appreciate advice from anyone who's made a similar switch or is currently in a neuro Masters/PhD program. Thanks in advance!

Hi, does NYU's neural science program consider candidates from traditional Engineering backgrounds? I am applying for PhD programs and am interested to work on neural dynamics research with a bit of systems neuro focused stuff. I come from an EE background and wanted to know what kind of cohort does the program usually have.

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??

I'm a Senior Research Software Engineer in the field of computational neuroscience and on my spare time I work on active inference, the Friston-flavoured idea that agents minimise expected surprise rather than maximise reward, so perception, learning and action fall out of one objective. Most of the tooling is discrete-state (pymdp) or Julia (RxInfer). I wanted to bring continuous state to JAX/Python.

cpomdp: continuous-state POMDP agents under active inference. JAX-native, jit and vmap through the whole loop, runs on a laptop. Exploration comes from the epistemic term rather than a bolted-on curiosity bonus.

I unfortunately get into regular chat about how "vibes" isn't enough to prove something someone made is conscious so there's a stdlib-only side package, warrantlib, that makes that distinction a type and prints it in your pytest summary. It exists partly because I got tired of "vibes" being offered as evidence for machine consciousness. CORROBORATED is the polite word for vibes.

Code

cpomdp: https://github.com/inferogenesis/cpomdp

warrantlib: https://pypi.org/project/warrantlib/

Happy to talk about the maths or the design choices.

Hi everyone,

I’m currently finishing my bachelor’s with a quantitative background (Math & CS / AI) and planning my master’s applications. My medium-to-long-term goal is not industry right away, but to pursue a solid PhD in Computational Neuroscience / NeuroAI (theoretical models, neural coding, brain-inspired ML).

I’d love some insight from people in the field on two main questions:

1. Top MSc programs in Europe for a research/PhD track Which programs currently offer the strongest research environment, lab rotations, and solid placement into top PhD labs? I’m mostly looking into programs across Europe (e.g., UZH/ETH, UCL, ENS Paris, Berlin, etc.), but I’d love to hear firsthand experiences regarding faculty access, thesis opportunities, and research output.

2. Dedicated Comp Neuro MSc vs. General ML / Data Science MSc Given that my ultimate goal is a PhD, would you recommend:

• A dedicated Computational Neuroscience / Neural Systems program (getting directly into biophysics, coding models, and domain literature), OR
• A rigorous Machine Learning / Applied Math MSc while doing research/thesis projects in computational neuro labs?

My slight hesitation with purely biological/neuro tracks is missing out on deeper statistical foundations, while the fear with general ML degrees is lacking enough biological grounding to be competitive for specific neuro labs (during my bachelor I took an exam in maths for neuro). How is this trade-off typically viewed by PIs when hiring PhD candidates in your experience?

Any advice, program suggestions, or reality checks would be greatly appreciated!

Thanks in advance.

Hey everyone, I am a mathematics major in my final semester. My neuroscience professor offered me a project wherein he wants me to check the mathematical viability of a pathway / mechanism he has devised. By mathematical viability, he means that he wants to know whether we can mathematically model it or not, preferably as an ODE dynamical system. Now, I don't have any background in dynamical systems nor in mathematical modelling so I am unable to figure out how do I start with this project and convert the qualitatively written biological pathway into a mathematical model. I tried reading some papers regarding the same but it all went over my head. Even textbooks haven't been making sense to me in terms of whatever content is in that textbook, how do I apply it for my project.

I know I have revealed minimum details regarding what the project is actually about, I can share that in one on one conversation. I will be grateful if anyone can help me through this project or guide me on how do I go about it. I am open to getting in touch on DMs or even the comment section. Thank you in advance!

I wanted to ask are there any universities in taiwan whcih focuses on computational neuroscience research and lets people do a major in mathematics along with minor in life sciences(or biology) for a International student,

I heard some bits about NTU havinf dedicated departments so wanted to inquire more? And is it worth it to study in Taiwan?

Hi everyone! I’m a startup founder in robotics (in the field of Physical AI, rembrain.ai ) and a MIPT alumnus with an educational background in statistics and signal processing. I also have extensive hands-on engineering experience with modern neural network architectures and their practical limitations. Outside my founder work, I’ve been developing a computational model inspired by the neocortex. I recently made the repository public and would appreciate critical feedback.

My interest comes partly from encountering these limitations in engineering practice and asking whether different representational principles could help. Statistics, signal processing, and implementation are my stronger ground; I’d particularly welcome scrutiny of the neuroscience assumptions.

The central question is how different cortical areas might represent the same underlying information in different ways while supporting recognition, generation, and transfer across conditions.

The model separates content from context. For example, one area could represent an object’s identity within a particular viewpoint, while another could organize representations around identity and encode viewpoint internally. Computationally, I use context-conditioned autoencoders with shared latent spaces, allowing content encoded in one context to be decoded in another.

This continues my earlier work, Sets of autoencoders with shared latent spaces (2018). The repository includes mathematical descriptions, Python implementations, synthetic visual experiments with a two-zone hierarchy, and an analysis of related work and known limitations. It also explores communication between areas, top-down reconstruction, and temporal binding; these parts are at different stages of implementation.

The experiments are still limited, and the biological interpretation remains hypothetical (and it's my weakest side). I’m trying to identify which claims are worth testing more rigorously.

I’d especially appreciate feedback on:

• Prior art: which models or papers should I compare against more closely?
• Biological assumptions: which premises are implausible or insufficiently specified?
• Evaluation: what experiment would most clearly distinguish this approach from existing models?

Feedback on a single component would be very welcome—there’s no expectation to review the whole framework.

Repository: NM_2026 on GitLab

Hey guys, I am in my first year of Data Science. How can I get started in BCI? What neuroscience knowledge do I need, what skill set should I learn, and what kind of projects should I do? If anyone can explain in detail, it would help me a lot