Fascination for research and development
We are living through one of the most interesting times in human history, a period that has provided us with access to unprecedented amounts of knowledge. The pace of change at the interface between science and industry is accelerating, with seemingly no end in sight – and the results are transforming major aspects of our lives. Digitalization, climate change and globalization are just a few of the many megatrends that are having a lasting impact on the age in which we live. So what does all this mean for us as a technology company?
At Bosch, we don’t have our eyes fixed solely on the future – we also strive to get to the heart of the issues that matter.
Searching for answers is a fundamentally important part of what we do – and it has formed part of Bosch’s DNA for over 130 years. We firmly believe that research is not an end in itself – a constant race to develop new technologies – but rather something that makes a tangible contribution to improving the quality of people’s lives. That belief is reflected in the words “Invented for life” that appear on our products.
Without research, there can be no progress. And without progress, we cannot improve the quality of people’s lives.
Get inspired by research at Bosch!
Get to know our researchers
Christina Johnston, Ph.D.
I manage and contribute to research in electrochemistry and materials, which includes fuel cells, batteries, and emerging topics. Previously at LANL, I focused on fuel cells, especially catalysts, supports, and electrodes. At Bosch, my team engages in electrochemical material and device development (e.g. fuel cell, battery, water purification), material property measurement, and advanced characterization.
Dr. Jan Hendrik Metzen
I am a Senior Expert in Robust Scalable Perception at the Bosch Center for Artificial Intelligence. My research is motivated by the desire to advance the understanding, robustness, and applicability of deep learning-based perception. In particular, I focus on evaluating and increasing the robustness of neural networks. While neural networks excel in classifying data coming from the distribution they have been trained on, they often perform poorly on data with slightly different properties. Addressing this shortcoming is crucial for applying neural network-based perception in safety-critical domains such as automated driving, where they will inevitably be faced with situations not encountered or foreseen during training.
I am a synthetic polymer chemist with a background in developing "smart" materials for drug delivery. Currently, I am a Lead Scientist in the Bioelectronics Group. Our team develops new technologies for next-generation medical diagnostics, especially for point-of-care applications. Our vision is to revolutionize healthcare by combining state-of-the-art engineering solutions in MEMS sensors, circuit design, and wireless communication, with biochemistry.