Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- DAAD
- Leibniz
- Nature Careers
- Fraunhofer-Gesellschaft
- Technical University of Munich
- Humboldt-Stiftung Foundation
- University of Münster •
- Helmholtz-Zentrum Geesthacht
- Charité - Universitätsmedizin Berlin •
- Dresden University of Technology •
- Forschungszentrum Jülich
- Hannover Medical School •
- Heidelberg University
- Heidelberg University •
- Karlsruhe Institute of Technology •
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute of Immunobiology and Epigenetics •
- Max Planck Institute of Molecular Plant Physiology •
- Max Planck Institutes
- Ruhr-Universität Bochum •
- TRON – Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz •
- Technical University of Darmstadt •
- Technische Universität Berlin •
- University of Bamberg •
- University of Bonn •
- 17 more »
- « less
-
Field
-
of research results or willingness to acquire such experience willingness to conduct a research stay for several month at another institute very good knowledge of MS Office, including Word, Excel and PowerPoint
-
, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
-
systems on different temporal and spatial scales. For our Research Group Applied Optimization we are looking for a PhD student: New Deep Learning - based Framework for Energy Modelling: Combination
-
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
-
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
-
respect. Embrace learning through discussions and believe in the creation of excellent and effective research through collaborative exchanges. Our doctoral students... Research work on exciting questions in
-
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
-
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