Sort by
Refine Your Search
-
generating a high-quality training dataset to support the development of the AI foundation model Contributing to the design and implementation of advanced deep learning architectures (e.g., Transformers, CNNs
-
PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
-
of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
-
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
-
, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
-
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
-
collaboration with Q.ANT GmbH in Stuttgart, a deep-tech company that develops photonic computing and photonic sensing products. The goal of this project is the development of highly integrated vapor cells with
-
programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
-
/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
-
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