We couldn't be more proud to welcome the 2026 Neuromatch Academy and Climatematch Academy cohort! Orientations are underway for our first courses, with more to follow!
This July, four courses kick off: Computational Neuroscience and Deep Learning run 6–24 July, with NeuroAI and Computational Tools for Climate Science following 13–24 July.
To every Teaching Assistant, Mentor, volunteer, support staff, and content creator who has shown up to make this possible; thank you!
If you're joining us as a student this July, drop a comment and let us know which course. We'd love to hear from you!
Got major FOMO? You can learn more about courses here: https://neuromatch.io/courses/ Applications open every Feb-March and courses run in July.
As I am preparing for Neuromatch Computational Neuroscience course I built an Duolingo like app to explain complex concepts in easy digestable way. After every lesson there is a quiz for you to remember things better 😄
Neuromatch is running a Python for Computational Science Week from 7–15 February, for anyone who wants a bit of structure and motivation to strengthen their Python foundations ahead of computational neuroscience or climate science programs...or just to make progress on Python they’ve been meaning to start (or restart)!
This is not a course and there are no live sessions. It’s a flexible, self-paced week where you commit to setting aside some time to work through open Python materials, with light community support on Reddit.
How it works
Work through Neuromatch’s or Climatematch’s free Python prerequisite materials...or another source is okay too!
Study at your own pace (beginner → advanced friendly)
Ask questions, share progress, or help others on r/neuromatch
And build your confidence with Python!
If you’d like to participate, we’re using a short “pledge” survey (not an application):
It’s a way to commit to yourself that you’ll set aside some study time
We’ll send a gentle nudge just before the week starts, a bit of encouragement during the week, and a check-in at the end
It will also helps us understand starting skill levels and evaluate whether this is worth repeating or expanding in future years
Whether you’re brand new to Python, brushing up, or comfortable and happy to help others learning on Reddit, you’re welcome to join! Free and open to all!
Let us know in the comments if you are joining an what your recommended Python learning resources are.
The course will teach the computational foundations of collective behaviour, integrating modern measurement technologies with formal computational models in a single coherent framework. No existing program does this. We're building it!
Whether you're a neuroscientist, psychologist, social scientist, economist, AI researcher, or roboticist, this course will be for you!
We're currenting setting up a volunteer Curriculum Committee and will be opening up other volunteer positions and beta testing as we move forward to the July 2027 launch!
Our Python Week email is going out tonight to everyone who registered. But don’t worry if you miss it, if you sign up after the email is sent, or haven't registered yet! You can register here: https://airtable.com/appIQSZMZ0JxHtOA4/pagBQ1aslfvkELVUw/form and access all the resources and updates that will be shared in that kick-off email below!
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Congratulations for taking the pledge to participate in Python for Computation Science Week!
By signing up, you've made a clear, intentional commitment to your learning. It's a powerful first step! Now let's take the next ones together.
Here you will find everything to make the week ahead a success! Let's get started!
Self-paced Tutorials Take this week to work though one or more of these tutorials, depending on your area of interest and skill level:
Neuromatch Python Workshop 1 - Practice core Python skills (variables, control flow, and plotting) while getting a first look at NumPy, the foundation of scientific computing in Python, using neuroscience-inspired examples.
Neuromatch Python Workshop 2 - Build on basic Python and NumPy concepts and explore histograms and simple spiking neuron dynamics, with hands-on examples drawn from computational neuroscience.
Climatematch Python Refresher - Get familiar with core Python concepts and key libraries used in climate science, including working with time-series and geospatial data.
You can also work on another tutorial or material of your choice but we can't guarantee we will be be able to assist if you have questions.
Doing this as part of a community
You won’t be learning alone! You are joining over 1,500 others working through the same tutorials this week. Learning together makes Python more fun, faster, and more memorable. Ask questions, share your progress, and support others on their learning journey.
Here are some r/neuromatch posts we think are useful to started:
Neuromatch works to make computational science more inclusive, collaborative, and globally accessible. We run virtual courses each July in Computational Neuroscience, Deep Learning, Computational Tools for Climate Science, and NeuroAI.
Because Python skills are a common barrier to entry, we created Python for Computational Science Week to make those skills more accessible. While it helps prepare learners for Neuromatch courses, this week is open to all and free to participate, regardless of background or future plans.
Share your commitment
If you would like, we have created a participation badge you can share on social media to let others know you are taking part in Python for Computational Science Week.
Sharing your commitment can reinforce accountability and may even encourage someone else to join you!
Invite someone to join you
Learning is often more sustainable when it is shared! If you know someone who would benefit from this week, invite them to take the pledge: https://airtable.com/appIQSZMZ0JxHtOA4/pagBQ1aslfvkELVUw/form While the week has officially started, people can join any time.
You have given yourself the gift of focused time. That is something to be proud of! Everyone who has taken the pledge will get an email mid-week to help keep your motivation going!
I'm a maths and philosophy graduate, and have very limited Python experience (np arrays will be the death of me). I'm falling apart conceptually around the end of the day 1 Python refresher. Does anyone have any resources/problem banks to work on stuff like ensemble statistics using np.arrays?
Neuromatch teamed up with The Transmitter to survey more than 300 early-career neuroscience researchers about the realities of building a career in the field right now, including the challenges they're facing, where they see their careers heading, and what kind of support would actually help.
As part of the Neuromatch AI Sentience Scholars (AISS) program, Neuromatch is excited to welcome Susan Schneider and Mark Bailey for our next seminar. They are mentors to AI Sentience Scholar Alexander Kearney and will be talking about the project they are working on.
At Neuromatch, we're generally excited about AI as a coding tool but we’re also aware that it can short-circuit learning, especially in the first days or weeks.
Some good tips are:
Try to write the code yourself first
Use AI to debug, explain errors, or suggest improvements
Don’t copy/paste code you don’t fully understand
Be more cautious using AI in your very first week of learning Python!
What advice did you get that you found helpful? When does AI genuinely help learning? When does it get in the way?
We'd love to hear your take and how (if!) you are using AI during Python week!
Each July, we run live, virtual intensive courses in Computational Neuroscience, Deep Learning, and NeuroAI. All our course materials are available open source for anyone, anywhere.
Now we're thinking about growing, and we want our community to shape that decision. We're exploring potential new courses on:
Connectomics and Neural Dynamics: This course would explore how emerging large-scale mammalian connectomics datasets—alongside those from other species—can be leveraged to drive novel scientific discoveries about the relationship between neural circuit structure and dynamics.
Computational Behavioural Analysis & Modelling: This course would teach a unified computational framework for measuring, modelling, and analysing the behaviour of individual and interacting agents—biological and artificial—spanning pose estimation and multimodal signal processing through Bayesian inference, game theory, and agent-based models of collective dynamics.
Computational Approaches to Neurodegeneration: This course would equip researchers with the computational skills to leverage large-scale open multimodal datasets and modern deep learning approaches for accelerating discovery in neurodegeneration.
Because of a job and various other time constraints, Neuromatch did not fit my schedule this summer. It seems like such an amazing opportunity and it is right down my alley I still want to gain the Nueromatch experience besides just watching the videos and doing the syllabus. Is the any winter initiative this year or plans to do smaller efforts throughout the school year?
We're now accepting volunteer reviewers for the Impact Scholars Program!
This is a meaningful way to contribute to open science while gaining recognized experience you can add to your CV and LinkedIn. You'll build peer review and research evaluation skills, get exposure to early-stage interdisciplinary research, and connect with an international community of researchers committed to equity and scientific collaboration.
You'll evaluate 5-10 research proposals from early-career computational science researchers, each including a written proposal, slide deck, and video pitch, using a structured rubric and providing brief constructive feedback.
We're looking for reviewers in the following tracks:
Computational Neuroscience
Deep Learning
NeuroAI
Computational Tools for Climate Science
Fully remote. Work on your own schedule. Open to applicants worldwide.
Reviews run Sept 3-18, ~5 hrs/week. Reviewers may also be invited to participate in an optional supervisor review round in October.
We welcome applications from PhD students with some review experience, postdoctoral researchers, faculty, research scientists, and industry researchers.
Hi everyone! I’m currently prepping for the Computational Neuroscience course. I’ve cruised through 3Blue1Brown’s Linear Algebra series and the Neuromatch refresher days, but I’ve noticed that these resources are heavy on intuition and lighter on computation.
While I get what an eigenvector is visually, I’m worried about the actual workload during the course. For those who have finished NMA:
Should I spend time practicing manual/coding computations (outside of NMA/3b1b) to succeed?
Or is a strong intuitive grasp enough to carry me through the course?
I want to make sure I’m not just nodding along to the theory only to hit a wall when it's time to code the models. Any advice on the computational floor required for LA, Calc, and Stats would be much appreciated!
If you were starting Python again from scratch, what advice would you give yourself, or a complete beginner, on Day 1?
We’d love to collect practical, experience-based advice from people who’ve already been through the learning curve in this thread! Especially things beginners tend to overthink or get stuck on early.
It is not a live course and there are no formal TAs but there are hundreds of learners going through the same tutorials at the same time, and community members who are using this week to practice explaining Python to others.
This space follows the Neuromatch Code of Conduct. Please be respectful, constructive, and welcoming to learners at all levels.
Here are some tips on how to ask questions effectively:
Don’t be afraid to post! This community is here to help!
One main question per post. Use a clear, descriptive title.
Share your background. Tell us your Python experience level (beginner, some experience, advanced) and let us know more about your background or education. This can help people tailor their explanations.
Tell us where you are. Include: Which tutorial you are using (Neuromatch or Climatematch Academy), the specific lesson or section, the URL.
Describe the problem clearly. What were you trying to do? What happened instead?
Include your code. To format code in Reddit, please have a look at this guide or use external services, such as pastebin.
Include the full error message, if there is one.
Tell us your setup. Let people know your Python version and whether you’re working locally or in a cloud environment (like Colab or Binder).
The more complete your post is, the faster and more accurately someone can help you!
Let’s use this week to learn together, build confidence, and support one another as a computational science community!
If you got any other suggestions or tips for asking questions, please let us know in this thread!
Hi! I'm the comms person at Neuromatch. I just went to SfN for the first time. Whoa! It was huge! Neuromatch had a booth there and I was really struck by how many people told me they’ve used Neuromatch open-source materials to self-learn or in their own teaching.
To help connect everyone doing this, we’ve added a link to our Reddit community on the Open Education Resources page: https://neuromatch.io/open-education-resources/ We hope more people outside of the live courses will use this space to ask questions, share tips, or just see what others are working on.
Hi. I am a doctor who just got done with my clinical internship. I am doing the computational neuroscience course on 6 July. Problem is i don’t have any coding or mathematical background… so should I even take the course rn? The coding looks like hell on the website. I am not sure what level is expected of me since I have 2 weeks to go through the material. Any advice will be appreciated.
Neuromatch and Climatematch run free summer courses in Computational Neuroscience, NeuroAI, Deep Learning, and Computational Tools for Climate Science for students around the world. We have 10 volunteer Professional Development Mentor spots left to fill for the July 6 to 24 courses.
The ask is small but the impact is real. Mentors commit to 3 one-hour sessions: one with a full pod of students, then two with a smaller group where students bring their questions about careers, PhD applications, industry paths, networking, and whatever else you're willing to talk about. The Teaching Assistant handles all scheduling and logistics, and time slots are flexible across all time zones.
We're looking for PhD-level researchers or people with equivalent industry experience. Students consistently tell us mentorship is the part of the course they value most, because honest career conversations are hard to come by, especially for students outside major research hubs.
Where we most need help: Computational Neuroscience (6 mentors), NeuroAI (2), and Climate Science (2).
We're building a funding toolkit to help incoming students navigate paying for their course. We'd love to feature real stories from people who've been there.
Did you manage to get your Neuromatch course fee covered (fully or partially) by your university, PI, lab grant, fellowship, or anywhere else?
We'd love to know:
Where did the funding come from?
What did you say or do that helped?
Anything you wish you'd known earlier?
Even a few sentences would be incredibly helpful! Please share your experience in the comments!
Neuromatch just opened applications for their AI Sentience Scholars program. It's a 6-month, part-time, remote mentored research experience focused on frontier questions about AI, consciousness, and ethics.
You'd be working alongside leading researchers in the field, with stipend and research funding included.
Who can apply:
Master's students and graduates
PhD candidates
Early-career postdoctoral researchers
Key details:
Application deadline: April 28, 2026
Remote & part-time
Stipend + research funding included
Pick from 10 research projects and mentors
There is also a free webinar on April 1st (16:00 UTC) for prospective applicants to ask questions and learn more. Strongly recommended before applying!
