Skip to main content

Our Fields Of Expertise

We work on a wide variety of cutting-edge AI research topics along with real-world applications ensuring the successful transfer into Bosch business.

Our Fields Of Expertise

We create differentiating solutions at the forefront of AI research. We focus on five main research areas, which have specific relevance for Bosch.​ Our research targets highly relevant future domains – including automated driving, manufacturing, robotics, embedded AI, healthcare, and many more – and demonstrates feasibility of novel technological concepts. We invent AI for life!

AI-Based Dynamics Modeling

Deep Learning

Deep Learning has delivered some of the most exciting advances in AI in recent years. However, deploying deep learning systems in the real world requires overcoming several challenges. Our research is constantly pushing boundaries of this technology to make deep learning systems in Bosch products safe, robust and efficient.​

Power Wall Renningen

Natural Language Processing ​

AI systems need to understand natural language to interact with humans and extract knowledge from unstructured sources. Therefore, we are developing novel methods for modern natural language processing and knowledge representation as well as reasoning techniques to automatically structure and process information within the whole Bosch organization.​

Large Scale Deep Learning

Neuro-Symbolic AI ​

Incorporating domain expert knowledge into neural networks' predictions and output is often challenging. Neuro-symbolic AI aims at integrating elements from both deep learning and automated reasoning, in order to achieve the best of both worlds: the flexibility and data-driven performance of deep learning with the logical consistency and grounded reasoning of symbolic methods.​​

Probabilistic Modeling

Probabilistic Modeling

Understanding complex processes and behaviors in the real world requires explicit representation and handling of uncertainties, and integration of formal domain knowledge. Therefore, we are developing new methods in probabilistic modeling that address these challenges, in particular for dynamical systems.​​

Reinforcement Learning, ​Control, and Optimization

Reinforcement Learning, Control, and Optimization

Many applications require more than just making predictions – we want to control or optimize. In particular, we focus on methods that efficiently learn from as little real-world trials as possible. Therefore, we are developing new methods for optimization and control, with a focus on reinforcement learning.​​