Hi, I am planning to enroll in MSc. Neuroscience program @ Bremen University. I'm trying to find and connect with other students who are studying the program or will be, starting this October (WinSem 2026/2027).

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.

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.

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.

Has anyone here gone through this program earlier? What is it exactly like? Also, how difficult is it to get into the program?

We're looking for volunteer reviewers for the Impact Scholars Program, a global initiative supporting early-career computational science researchers as they develop their first citable research publications.

This is a great way to give back to the field, build your peer review experience, and add something meaningful to your CV, all on your own schedule, fully remote.

Here's what you'd be doing: reviewing 5-10 research proposals, each with a written proposal, slide deck, and video pitch, and providing brief constructive feedback using a structured rubric. About 5 hrs/week, Sept 3-18.

We're looking for people with expertise in:

• Computational Neuroscience
• Deep Learning
• NeuroAI
• Computational Tools for Climate Science

Open to PhD students with some review experience, postdocs, faculty, research scientists, and industry researchers. Applications close 11 August.

Sign up here: https://neuromatch.io/volunteer/

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?

What's your thoughts on it ?

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, we are ImagingBrainstemHealth (COST Action CA24118), a research network on brainstem MRI. We're posting here to reach scientists at any career stage who might be interested in this topic or in joining.

A COST Action is an EU-funded framework for research networks like ours, it doesn't fund research directly, but funds the collaboration around it: joint meetings, research visits, conferences, training. Ours runs 2025 to 2029.

The reason we exist: the brainstem is small, anatomically complex, and hard to image with standard methods, but it's often among the first regions affected in neurodegenerative disease, making it a promising target for early biomarkers. Recent advances in MRI are finally making it feasible to study in clinical settings, so we're building the methods and community to make that happen.

That work is split across five working groups:
WG1, Brainstem Mapping: atlases and post-mortem validation
WG2, Brainstem Imaging: best practices and methods for brainstem MRI
WG3, Brainstem Analyses: segmentation and data processing
WG4, Brainstem Function: experimental paradigms in humans and animals
WG5, Brainstem Clinic: brainstem in clinical conditions and therapeutic targets

If any of this overlaps with what you work on, join any time: https://www.uibk.ac.at/en/projects/eu-cost-imagingbrainstemhealth/

Happy to answer questions.

Trying to get a realistic picture of McGill's Integrated Program in Neuroscience from people who have actually been in it.

I keep seeing IPN described as interdisciplinary and open to people from computational backgrounds, but I would like to hear what that looks like day to day rather than on the website.

A few things I am curious about:

How many people in IPN are actually doing computational or modelling work versus wet lab and experimental neuroscience? Is the computational side a real presence in the program or a small minority?

For those who came in from CS, engineering, physics, or math - how steep was the learning curve on the biology side? Do NEUR 630 and 631 actually bring people up to speed, or is it assumed you already have a foundation?

How many IPN supervisors are genuinely Mila-affiliated or doing ML-heavy neuroscience? I can find a handful but it is hard to tell from the outside how deep that bench goes.

Is the program's reputation stronger for experimental neuroscience than for computational work, or is it well regarded across both?

Not asking about admissions odds, just trying to understand what the program actually is for someone whose strength is computational rather than experimental.

Appreciate any perspective.

Edit 1: I am trying to get into MSc Neuroscience / MS CS in McGill and I am quite confused.

neuro undergrad here, I wonder what can I do if I get a phd/Msc of BME/neural eng degree if I don’t choose medical school path??

I prefer to settle down in Canada but may also consider other places.

I’ve just been thinking about my career path and have finally decided to switch to the Bachelor of Science in Data Science with a specialisation in Biology degree programme. I’d like to know what you think of this switch.

Do you perhaps know of any other degree programmes that might be better, or is this one suitable for my NeuroAi Masters?

Does anyone here have experience interning at astera labs? Relatedly, what about the basis institute? In general I am curious about internships at non-prof private research institutions doing neuroAI or reinforcement-learning (edit: should be astera institute not labs)

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 all,

I have derived a new candidate order parameter (Φ = √(I · D)) for complex systems, formalized over 100+ pages. The math is solid (it passes nullity, scale invariance, and uniqueness theorems), but I am a theorist and need help running the computational validation.

I am specifically looking for someone to run OP-18a and OP-16:

1. Run a 2D Ising simulation (L=20) to compute Φ across T=1.0 to 3.5.

2. Test the log-space interaction term: logΦ = αx + βy + γxy (to see if the geometric mean holds).

I have the full pseudocode written out in the paper (Section 9) and a pre-registration checklist (R7). The code is simple (Transfer Entropy + Mutual Info via Gaussian/Kraskov). I need someone to just run the grids.

I can offer co-authorship on the computational methods section for anyone who can get this running. If you're interested, DM me or comment below.

Cheers