About AI@RUB
At RUB, we shape the future of AI across all disciplines – aiming to make it an understandable, trustworthy and usable technology for all.
Our research seeks to uncover new solutions to information processing in intelligent systems, builds trust through increasing transparency and explainability and explores application areas to transfer foundational insights into real-world results.
We passionately teach our students and expose them to the most exciting advances in AI.
We foster a vibrant network that encourages creative, out of the box solutions. A number of events supports interdisciplinary collaborations.
Research
From understanding how the brain processes information to designing fair and explainable algorithms, our research spans across the full spectrum of artificial intelligence — theoretical foundations, technical innovation, and real-world applications.
Our professors lead cutting-edge research groups in areas such as machine learning, neural systems, information security, and the societal implications of AI. Together, they shape a research landscape that is collaborative, interdisciplinary, and driven by curiosity.
Explore our AI-related chairs @ RUB
Socio-Technical System Design and Artificial Intelligence
led by Prof. Dr. Christian Meske
Teaching
Our teaching combines deep theoretical knowledge with practical experience, covering a wide spectrum of AI—from machine learning and natural language processing to neural computation and AI security. We emphasize both technical mastery and societal responsibility, ensuring that students understand not only how AI works, but also how it impacts the world.
Teaching Areas
General AI
This collection of courses offers a broad foundation in Artificial Intelligence and related disciplines. It spans core areas like AI, Data Science, and Advanced Python Programming, along with specialized topics such as Neural Data Science, Mathematics for Modelling, and Neuroinformatics. Courses on Formal Verification and AI Ethics ensure a balance between technical depth, reliability, and responsible application.
Machine Learning
These courses explore the principles and applications of machine learning, focusing on how systems can learn from data to make predictions and decisions. Advanced areas such as reinforcement learning and machine learning security address how to build robust, reliable, and secure learning systems that resist adversarial attacks and data manipulation.
Computational Neuroscience
Computational neuroscience is the study of how the brain processes information using mathematical models, computer simulations, and theoretical analysis. It aims to understand the principles underlying neural activity, learning, and behavior by linking brain structure to function. This field bridges neuroscience, computer science, physics, and mathematics to develop models that can both explain biological neural systems and inspire artificial intelligence.
Events
Our events bring together students and researchers to explore the latest trends in AI, spark new ideas, and build lasting collaborations. Whether you’re diving deep into a specialist lecture or meeting peers over coffee, there’s always something happening in our vibrant AI community.
