Here you will find an overview of the projects we are currently funding. On average, about 150 projects are being funded. Smaller funding projects are sometimes not described individually.
206
ongoing projects
How much pre-programmed knowledge, for example in the field of agricultural science, does an AI need in order to better classify observations? An interdisciplinary research team at the TU Kaiserslautern is dealing with these and similar questions.
In E4SM, innovative methods for the development, implementation and operation of machine learning-based assistance systems for smart manufacturing in small and medium-sized enterprises are being researched.
Tobias Buck holds a doctorate in astrophysics. During his studies at the University of Heidelberg, he focused on robotics, machine learning and space exploration. Most recently, he worked as a postdoc at the Leibniz Institute for Astrophysics in Potsdam.
In the Serious Game Lab, therapeutic games for seniors are developed and analyzed. In addition to providing information, patients are motivated to repeat exercises through games.
The HighAR project is investigating how augmented reality can be used to specifically control visual attention, e.g., through color changes. This can lead to faster data acquisition by the user.
The Prisma grant enables Prof. Dr. Doris Aschenbrenner, Professor of Digital Methods in Production at Aalen University, to expand her research on Industry 4.0 to include important socio-economic aspects.
By combining various artificial intelligence methods, the aim is to develop intelligent sensors and actuators that adapt autonomously to changes and thus enable more flexible production.
Prof. Claudia Nass Bauer, Professor of Design Strategy at Mainz University of Applied Sciences, is researching the development of methods for value-oriented interdisciplinary collaboration in the development processes of data-intensive software systems.
A research training group is investigating how conclusions are drawn in machine learning, taking into account the uncertainties involved.
Methods from machine learning for big data are to be applied to questions in atmospheric physics. Among other things, the representation of clouds in climate models and the predictability of weather situations will be worked on.