AI & ML Research Events in Europe
Europe's AI and ML research scene is a long tail of university labs, community research groups, and practitioner-run meetups that together punch well above their weight globally. This page covers the research end of the AI/ML event calendar: deep-learning meetups, computer vision and NLP groups, paper-reading clubs, and research-oriented conferences. It's aimed at ML engineers, applied researchers, and PhD students who care about how the models work, rather than how to integrate them into a product (that's the Applied AI category).
Anchor events include the MLcon series (Berlin, Munich, Amsterdam, London), PyData conferences, and regional deep-learning meetups like the Vienna Deep Learning Meetup. Quantum AI and quantum-computing events sit here too, since the European quantum community is research-heavy and overlaps meaningfully with the ML research crowd. Topics cover training and serving frameworks, fine-tuning technique, evaluation, quantization, model architectures, and the infrastructure that makes experimentation tractable.
Brainberg aggregates these into a single chronological European view. For the deluge of "how to ship a feature with an LLM" events, see the Applied AI category instead.
Upcoming events
GPU, CUDA, and PyTorch Performance Optimizations
Zoom link: https://us02web.zoom.us/j/82308186562
Talk #0: Introductions and Meetup Updates
by Chris Fregly and Antje Barth
Talk #1: GPU, PyTorch, and CUDA Performance Optimizations
Talk #2: GPU, PyTorch, and CUDA Performance Optimizations
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
Github Repo: http://github.com/cfregly/ai-performance-engineering/
O'Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/
YouTube: https://www.youtube.com/@AIPerformanceEngineering
Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm
Python ML & AI Bootcamp: 1 Day Practical Workshop in Odense
Odense, 🇩🇰 Denmark
Dive into ML and AI using Python—master supervised and unsupervised learning, model evaluation, and neural network basics in one day.
Group Discounts:
- Save 10% when registering 3 or more participants
- Save 15% when registering 10 or more participants
About This Course
Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session
Course Overview:
The Machine Learning & AI in Python course empowers you to understand, build, and evaluate predictive models using Python. You will learn the fundamentals of supervised and unsupervised learning, model evaluation metrics, feature engineering, and get a glimpse into neural networks and deep learning. With practical hands-on exercises, this course prepares you to transition from theory to real-world machine learning applications.
Learning Objectives:
By the end of this course, you will:
- Understand core machine learning concepts and workflows
- Build supervised and unsupervised models using scikit-learn
- Evaluate model performance using appropriate metrics
- Apply feature engineering techniques to improve predictions
- Gain basic knowledge of neural networks and deep learning
- Use Python for real-world AI and ML problem-solving
Target Audience:
Data scientists, ML engineers, developers, and advanced Python users.
Why is it right fit for you:
If you’re looking to take your Python programming skills into the realm of machine learning, this course is ideal. With a strong focus on applied learning and best practices, you’ll build models and analyze datasets that mirror real-world challenges. Our experienced instructors make complex concepts like algorithms and neural networks accessible through hands-on examples. This course helps you build confidence in working with machine learning tools and prepares you for advanced AI workflows.
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Reinforcement Learning – Introduction
Potsdam, 🇩🇪 Germany
AI Workshop | How Agents Learn by Trial and Error
Most AI systems learn from existing data. But what if no data exists? And what if even humans don’t yet know how to solve the problem in question?
In this beginner-friendly workshop, you will explore how Reinforcement Learning (RL) enables machines to learn from experience through trial and error, guided only by rewards. Hands-on coding exercises will demonstrate how interaction replaces supervision and how feedback drives "intelligent" behaviour — much like learning in animals and humans.
You will learn how agents interact with their environment, make decisions, and improve over time. The workshop uses interactive and practical formats to build an intuitive understanding of key RL concepts such as agent–environment interaction, the exploration–exploitation trade-off and the role of reward design.
The workshop provides orientation in a field that plays a central role in domains such as robotics, logistics, control tasks and game development.
Requirements
Basic knowledge of Python is recommended.
No prior experience with Reinforcement Learning is required. This is an introductory workshop and not suited for participants with existing RL knowledge.
Please bring a laptop to the workshop.
Registration & participation
Registration is binding. If you are unable to attend, please cancel your ticket in good time to allow other participants to take your place.
Important Information
Photographs, audio recordings and films will be made during the event. By participating in the event, you agree that photos, audio and video recordings in which you are recognizable may be published as part of the public relations work of the AI Service Center Berlin-Brandenburg and the HPI.
The AI Service Centre Berlin-Brandenburg is a project of the Federal Ministry of Research, Technology and Space. Its aim is to lower the barriers for using AI in business and society.
Are you interested in our free workshops and other events that we offer? Then sign up for our newsletter.
AI, ML, and Computer Vision Meetup
Artificial Intelligence (AI) meetup Online
Reinforcement Learning – Implementation
Potsdam, 🇩🇪 Germany
AI Workshop | Designing and Building RL Environments
Reinforcement Learning (RL) offers a framework for tackling sequential decision-making problems in areas like robotics and energy management. But how do you turn a real-world problem into a well-defined RL task?
In this workshop, we move from foundational RL concepts to practical application. In this interactive workshop, we use a simple illustrative example, such as managing an energy storage system, to demonstrate the step-by-step process of translating a given task into a RL setup.
You learn to assess when RL is suitable and, if so, how to design the environment by defining state and action spaces and reward functions, and how to implement it using Python's Gym library.
The goal is to introduce you to the workflow, show you how to evaluate and adjust your framework, and spark curiosity to tackle your own problems with RL.
Requirements
Basic knowledge of Python is recommended.
Basic RL knowledge is recommended (e.g. being familiar with these terms: agent, state, action, reward, policy).
Please bring a laptop to the workshop.
Registration & participation
Registration is binding. If you are unable to attend, please cancel your ticket in good time to allow other participants to take your place.
Important Information
Photographs, audio recordings and films will be made during the event. By participating in the event, you agree that photos, audio and video recordings in which you are recognizable may be published as part of the public relations work of the AI Service Center Berlin-Brandenburg and the HPI.
The AI Service Centre Berlin-Brandenburg is a project of the Federal Ministry of Research, Technology and Space. Its aim is to lower the barriers for using AI in business and society.
Are you interested in our free workshops and other events that we offer? Then sign up for our newsletter.
AI, ML and Computer Vision Meetup
Artificial Intelligence (AI) meetup Online
4th Tech Summit on Artificial Intelligence & Robotics
Roissy-en-France, 🇫🇷 France
Join us in Paris, France, on September 28-30, 2026, to meet experts from industry & academia. This summit is offered in person and online.