Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Nature Careers
- Leibniz
- Technical University of Munich
- Forschungszentrum Jülich
- Heidelberg University
- Fraunhofer-Gesellschaft
- DAAD
- European Magnetism Association EMA
- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute for Plasma Physics (Garching), Garching
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Technische Universität München
- University of Tübingen
- WIAS Berlin
- 4 more »
- « less
-
Field
-
) infrastructures is advantageous Excellent analytical and problem-solving capabilities Proven track record of publishing in reputable scientific journals and at the leading conferences Ability to effectively work in
-
at Heidelberg University and Stanford University. The group works on a wide variety of research, with foci being medication effectiveness, health services research, and population health issues. One key data
-
documents cannot be considered. Foreign qualifications must carry out an equivalence test in Germany, which is subject to a fee. This must be presented in the event of a later hiring: https://zab.kmk.org/en
-
) activities, in close collaboration with the JPI Oceans Knowledge Hub on Changing Marine Lightscapes. Your work will provide a structured framework to assess the effects of changes in the quality
-
with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
-
imagery Engineer advanced architectural components and workflows for efficient and effective foundation model training and fine-tuning Devise data curation and balancing schemes Rigorously assess model
-
with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
-
, and energy systems into a comprehensive bio-based circular economy. We develop and integrate techniques, processes, and management strategies, effectively converging technologies to intelligently
-
and result in negative consequences, for example, racial or gender bias, discrimination, or surveillance. This project advances collaborative, qualitative research to better understand who and what AI
-
interactions with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D