Google Summer of Code 2026 proposed ideas

Contributors interested to participate should check which of the following projects fits their interests and skills.

Τo communicate with the mentors and ask questions about the projects, students should subscribe to this list and post relevant questions. Please follow the Proposal Template

For practical information, developers should visit this page.

FOSSBot Platform: Simulation Enhancements and AI Integration

Brief Explanation

The FOSSBot Platform aims to enhance robotic simulation environments by integrating AI-driven capabilities and improving simulation realism. The project focuses on extending existing open-source robotic simulation tools with advanced features that support experimentation, education, and research in autonomous systems. Emphasis is placed on modularity, extensibility, and reproducibility within open-source ecosystems.

Expected Results

The expected outcomes include enhanced simulation modules, AI-assisted decision-making components, improved documentation, and example use cases. The project will deliver code contributions upstream, along with benchmarks and demonstrations showcasing the improvements in robotic simulation fidelity and usability.

Duration of the Project

Large Project - 350 hrs

Related Repositories

https://github.com/eellak/fossbot-platform https://github.com/eellak/fossbot https://fossbot.gr

Knowledge Prerequisites

Applicants should have good knowledge of JavaScript, Python, robotics simulation environments, and basic AI/ML concepts. Familiarity with open-source workflows and collaborative development is required.

Mentors

Christos Chronis, Eleftheria Papageorgiou, Irida Ntinou

AI assisted KMC

Brief Explanation

This project explores the integration of machine learning techniques into Kinetic Monte Carlo (KMC) simulations. The goal is to accelerate simulations and improve predictive accuracy by leveraging AI models trained on simulation data. The project targets scientific computing and materials science applications.

Expected Results

Deliverables include AI-augmented KMC algorithms, performance evaluations against traditional methods, and a reproducible pipeline for training and inference. Documentation and example experiments will accompany the final implementation.

Duration of the Project

Large Project - 350 hrs

Related Repositories

https://github.com/nixeimar/Apothesis

Knowledge Prerequisites

C++, working knowledge of machine learning techniques, numerical methods, and statistical modeling. Prior experience with scientific simulations is a plus.

Mentors

Cheimarios Nikolaos, Vissarion Fysikopoulos