Sort by
Refine Your Search
-
The »Q uantum Computing« team at the Fraunhofer Institute for Industrial Engineering Engineering IAO is looking for motivated talents who want to work at the interface of artificial intelligence
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
students who would like to write their final thesis in the field of machine learning / computer vision. The primary goal of this master’s thesis is to develop an algorithm that can accurately and efficiently
-
," and "Computational Methods." Within the "Computational Methods" group, the focus is on the design and simulation of laser beam sources and laser processes, using numerical methods and modern AI
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
promising alternative to classic methods of process optimization due to its adaptive decision making and data efficiency. In this thesis, a BO algorithm for the optimization of laser processes is to be
-
environment. We are creative, we shape technology, we design products, we improve processes, we open up new paths. Our clients value our modelling competence, algorithms and software products. We
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we