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?

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]

What's your thoughts on it ?

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

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.

Hi,

I am a student starting third year from a reputed university in India, I am pursuing a bachelor in EE and recently gained interest in this field. I checked the requirements but they say that BCCN values math grades a lot and I havce Bs and Cs in my math courses and have a very average gpa. I have started research in computational Neuroscience under a professor at my college. Do I have a chance, if yes then what to do?

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.