This page contains general information on European Summer of Code 2026:
- European Summer of Code: background and org structure
- For applicants: how to apply, list of projects 2026
- For projects: how to join, timelines
ESoC webpage - ESoC on LinkedIn
Projects from previous years: 2025
- 2026-02-18: Batch 1 projects released!
- 2026-01-05: Applicant Guide, Sponsor Guide 2026 published
- Information on European Summer of Code
- Timelines 2026
- Applicant Guide
- Sponsor and Project Guide
- 2026 Project List
European Summer of Code (ESoC) funds stipends for contributors new to open source, and matches open source projects and applied AI projects throughout Europe. We are much indebted to the Google Summer of Code for inspiration.
The goals of ESoC are threefold:
- provide opportunities for junior developers to engage with open source worldwide
- link the worldwide open source landscape with private and public sector project throughout Europe
- provide support to the wider open source ecosystem
In particular, we expressly help applied projects to connect with relevant open source, open source projects to connect with applied projects and sponsors, and applicants to work on open source directly in an applied context.
- participants apply for projects with open source libraries or applied partners
- public and private sector sponsors submit projects
- open source projects apply for support
- hubs throughout Europe carry out the matching process
ESoC is organized in a decentral and distributed fashion, by hubs. Current hubs are:
- France: probabl - the scikit-learn company
- Germany: German Center for Open Source AI
- Italy: Fondazione Bruno Kessler
We are actively looking to expand our network of hubs.
Get in touch on LinkedIn to discuss details.
We are actively looking to expand our network of affiliated open source projects.
Projects may affiliate with one of our hubs for streamlined operational integration and funding opportunities.
Fill out our open source project onboarding form to join!
Currently affiliated projects:
- AI-on-Demand
openMLpixipgmpypykalmanpytorch-forecastingpytorch-tabularrattlerscikit-baseskprosktime
dsip-tsGradient-Free-Optimizershyperactiveprophetverse
2026 projects will be released in two larger batches, on Feb 18 and Mar 19, and on a rolling basis thereafter.
An information event for applicants and interested organizations will take place on January 30, 11-14 UTC.
Timelines for the two batches are below.
The 2026 ESoC information event sponsored by the AI-on-Demand Platform project, the European vision for an open source AI platform.
The event will feature:
- general information on the programme
- applicant guide and Q&A
- organization and sponsor guide and Q&A
- advice from previous year's mentors and successful applicants
Location: AIoD discord, events channel
- February 18, 18:00 (UTC). Applications open.
- March 18, 18:00 (UTC). Applications must be submitted by this deadline.
- Applicant evaluation, reviews, interviews
- April 15 - Projects start
- March 19, 18:00 (UTC). Applications open.
- April 16, 18:00 (UTC). Applications must be submitted by this deadline.
- Applicant evaluation, reviews, interviews
- May 15 - Projects start
- larger batches of 2026 projects will be released on February 18 and March 19.
- Individual 2026 projects are also released on a rolling basis.
- projects have their own timelines and may come with their own terms of conditions
- project specifics are listed in the project card, in the list of released projects below
Applicants need to do two things:
- register for ESoC through the ESoC applicant registration form
- follow any additional "how to apply" steps from the project card of your preferred projects
- The project card may require you to fill out a second form, or complete an additional task
- The project card may also simply say that filling out the ESoC form suffices (as long as you mention the project)
- While there is no limit in the number of projects you can apply for (or penalty), we recommend to focus on at most 3. Applying to too many projects will reduce your time per application and impact application quality.
The deadline for completing both ESoC application form, and the project specific application steps, by the project application deadline. The project application deadline varies by project, and is specified in the project card, below.
- Please ensure your submitted e-mail address is correct. Letters will be sent to this e-mail address.
- Please use the same name and e-mail for any fields and forms throughout the process
- Ensure to note down relevant date periods in advance, and ensure to check your e-mail in those periods.
- In case of submitted forms, most forms allow you to update the submission up until the deadline.
- If this is not the case, simply submit another application under the same name. The last submission received before the deadline for a given project counts.
- note that the last received forms count here - not submitted forms which are not received due to technical error, or received too late.
- So ensure you submit something early, as some submission systems may overload or not work properly in the last few hours before deadlines.
Applicant Q&A in the discussion forum
You will receive a letter from ESoC on your application(s) no later than 6 weeks after the application deadline of the project you applied to.
This can be:
- acceptance to one or multiple projects. This means you are offered a slot.
- waitlist status for one or multiple projects. This means you may be offered the slot if higher ranked applicants withdraw.
- In this case a follow-up letter with acceptance or rejection will be sent at a later time.
- rejection from one or multiple projects
The letter will be sent to the e-mail address you registered with.
In case of acceptance, you will need to reply to the letter within 1 week of receipt.
Please note: once you accept the offer from a project, any pending applications of yours to other projects in 2026 are automatically withdrawn, including those from later batches where the outcome may not yet be clear, or projects where you are on the waitlist.
European Summer of Code is a paid stipend programme.
- ESoC stipend holder participate in onboarding events, regular stand-ups and mentoring throughout the period collaboration is virtual, via GitHub and Discord. Some partner organisations may optionally offer hybrid or in-person collaboration.
- full time dedication to the stipend project is expected throughout the mid-year period (12 calendar weeks), excluding 5 working days of pause
- flexible time arrangement are possible, at discretion of partner organisations - but 3 months continuous full time availability is normally expected
- if not stated otherwise, projects are accompanied by a pro-rata based on a flat stipend of 4.800 (four thousand and eight hundred) Euro for 3 months full-time equivalent. Disbursement is via stipend through the ESoC hub.
- projects may offer additional unpaid mentoring slots at their discretion.
- participation does not constitute formal employment, nor are there delivery expectations.
- applied projects (only those marked explicitly as "applied projects") may require signing a data sharing agreement. In this case, the agreement will be shared and can be reviewed by candidates before accepting the stipend.
- Private and public sector organisations wishing to host a project should submit a project sponsor form.
- open source projects should submit an open source project form.
One of our hubs will be in touch shortly after submission, for review. In case of positive decision, we will work with you to prepare your project for release or onboarding.
Finalization for sponsored projects requires:
- choosing a funding model:
- option A: donation for stipend. This is easiest, with low contractual complexity: you donate to a hub, and the hub contracts with the participant. The "stipend" requires that a significant portion (but not all) of the outputs are in the open domain, e.g., contributions to an open source project or a research report.
- option B: paid internship. You can use your own contract template, in this case the hub will only act as matchmaker, and you will diractly contract with applicants and manage any tax and visa requirements.
(this information queried via the project sponsor form)
- completing the project card, see details below and example project cards
- this includes finalizing an application process with your hub. "Default" processes are available, these select for generic AI, python, and open source capabilities.
- for data driven projects, it will also require finalizing data batches and data sharing processes before the project starts
Open source projects should let us know via the form what they are looking for:
- sponsoring for development (internal project)
- matchmaking with applied AI projects
- contributor onboarding
Open source project should:
- create a list with development projects
- ensure that mentors are available
- communicate their capacity for stipend slots and mentored slots
(this information queried via the open source project form)
Project cards should include:
- project title, project logos
- one-sentence description
- paragraph: project goals
- paragraph: about the organization (optional: logo)
- paragraph: technical details (very short - link details)
- clear "HOW TO APPLY" info, deadlines
- optional: eye catcher image
- optional links to: more details, data (where applicable), videos or presentations
Some examples are in the project list below.
- First batch of projects below.
- Second batch of projects announced March 19
The open platform for AI on demand.
Contribute to ai-on-demand – the European vision for an open AI portal!
ai-on-demand is an open-source initiative developed and maintained by a growing community of contributors focused on accessible AI assets.
The AI-on-demand metadata catalogue has the ambition to provide easy access to open source AI resources across the ecosystem.
Multiple projects are available within the ai-on-demand ecosystem. You can contribute to catalogue functions, backend maintenance, performance optimization, service improvements, documentation improvements, or propose your own project idea by opening an issue in the repository.
HOW TO APPLY: mention ai-on-demand as an interest on your ESoC application (you can update this if you already applied).
Then, submit a pull request addressing a good first issue by March 18. The ai-on-demand maintainers will contact you with further details.
The following repositories are valid contribution targets for the application phase:
- https://github.com/aiondemand/aiondemand
- https://github.com/aiondemand/AIOD-rest-api
- https://github.com/openml/openml-python
- https://github.com/openml/server-api
- projects supported by GC.OS - https://github.com/gc-os-ai/#projects-supported-by-gcos
Application details:
- Deadline: March 18, 18:00 UTC
- Sponsor: funding by AI-on-Demand project (Horizon Europe)
- Hub: German Center for Open Source AI
- T&C: Pro-rated stipend disbursed by German Center for Open Source AI
The framework for causal inference, causal structure learning, and causal simulation.
Contribute to pgmpy - the causal learning package! Help implement new types of causal models, and increase interoperability of, and within, the package!
pgmpy is an open-source project backed by an active community of researchers and developers.
It is the major causal modelling package not affiliated with a single commercial entity, with a distributed user and maintainer base.
GitHub repo · Discord · Documentation
Multiple projects are available: project ideas 2026, mentored projects 2026 - you can also suggest your own project, by opening an issue.
HOW TO APPLY: mention pgmpy as an interest on your ESoC application (you can update this if you already applied).
Then, make a pull request on a good first issue by March 18. You will be contacted with further details by the pgmpy team.
- Deadline: March 18, 18:00 UTC
- Sponsor: Zerodha
- Hub: Radboud University
- T&C: pro-rated stipend disbursed by German Center for Open Source AI
The Python framework for Generalized Additive Models (GAMs).
Contribute to pyGAM – the interpretable machine learning library for additive modeling! Help implement new spline terms and distributions, improve model diagnostics and visualization tools, and expand compatibility with the broader scientific Python ecosystem.
pyGAM is an open-source project focused on making Generalized Additive Models accessible, practical, and production-ready in Python.
It provides a flexible and user-friendly interface for fitting GAMs with automatic smoothing parameter selection, built-in regularization, and rich visualization utilities. The project is community-driven and welcomes contributions ranging from core algorithmic improvements to documentation, testing, and examples.
Multiple projects are available: project ideas 2026.
HOW TO APPLY: mention pyGAM as an interest on your ESoC application (you can update this if you already applied).
Then, submit a pull request addressing a good first issue by March 18. The pyGAM maintainers will follow up with further details.
- Deadline: March 18, 18:00 UTC
- Hub & Sponsor: German Center for Open Source AI
- T&C: Pro-rated stipend disbursed by German Center for Open Source AI
Rust crates for fast handling of conda packages
rattler is a library that provides common functionality used within the conda ecosystem.
Projects are available to implement build process mechanisms, for instance via parallelization
The goal of rattler is to enable programs and other libraries to easily interact with the conda ecosystem without being dependent on Python. Its primary use case is as a library that you can use to provide conda related workflows in your own tools.
HOW TO APPLY: mention rattler on your ESoC application (you can update this if you already applied).
Then, make a pull request on a good first issue by March 19. You will be contacted with further details by the rattler team.
See here for a list of good first issues.
- Deadline: March 19, 18:00 UTC
- Sponsor: prefix.dev
- Hub: German Center for Open Source AI
- T&C: pro-rated stipend disbursed by German Center for Open Source AI
The unified framework for probabilistic supervised machine learning in Python.
Contribute to skpro! skpro extends the scikit-learn ecosystem with a unified API for probabilistic regression, distributional prediction, survival analysis, and related uncertainty-aware learning tasks. Exciting projects are available in areas such as probabilistic deep learning, calibration, distributional metrics, composite models, and tighter interoperability with the scientific Python stack.
skpro provides consistent abstractions for predictive distributions, probabilistic metrics, and meta-estimators, enabling researchers and practitioners to build, evaluate, and deploy models that quantify uncertainty in a principled way. The project is openly governed and follows a mission of technical integration and content neutrality.
GitHub repo · Discord · Documentation
Multiple projects are available within the skpro ecosystem, including new probabilistic estimators, improved benchmarking tools, enhanced distribution objects, and ecosystem integration.
HOW TO APPLY: follow the sktime application guide - this includes projects for skpro.
- Deadline: March 19, 18:00 UTC
- Hub & Sponsor: German Center for Open Source AI
- T&C: Pro-rated stipend disbursed by German Center for Open Source AI
The unified framework for machine learning with time series
Contribute to sktime! sktime offers a unified API for forecasting, classification, detection, and other learning tasks.
Exciting projects are available in the areas of deep learning, foundation models, probabilistic forecasting, and more.
sktime is an open-source project backed by an active community of researchers and developers. It is openly governed, and follows a mission of technical integration and content neutrality.
GitHub repo · Discord · Documentation
Multiple projects are available: project ideas 2026
HOW TO APPLY: follow the sktime application guide
- Deadline: March 19, 18:00 UTC
- Hub & Sponsor: German Center for Open Source AI
- T&C: pro-rated stipend disbursed by German Center for Open Source AI
Deep learning for time series forecasting made easy with PyTorch
pytorch-forecasting is a Python library built on PyTorch, designed to simplify training and using deep learning models for time series forecasting.
Projects are available, focusing on improving model interfaces, upgrading the package to include foundation models, and more!
pytorch-forecasting is maintained by the sktime community in collaboration with GC.OS and FBK (Fondazione Bruno Kessler, ESoC hubs), a merge with dsipt-ts is planned for the upcoming 2.0 release.
GitHub repo · Discord · Documentation
Multiple projects are available: project ideas 2026
HOW TO APPLY: follow the sktime application guide - this includes projects for pytorch-forecasting.
- Deadline: March 19, 18:00 UTC
- Hub & Sponsor: GC.OS, Fondazione Bruno Kessler
- T&C: pro-rated stipend disbursed by German Center for Open Source AI
