Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Munich
- Leibniz
- Nature Careers
- Forschungszentrum Jülich
- Heidelberg University
- Fritz Haber Institute of the Max Planck Society, Berlin
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- ; Technical University of Denmark
- DAAD
- Free University of Berlin
- Max Planck Institute for Biology Tübingen, Tübingen
- Max Planck Institute for Dynamics and Self-Organization, Göttingen
- Max Planck Institute for Extraterrestrial Physics, Garching
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- Max Planck Institute of Biochemistry, Martinsried
- University of Tübingen
- WIAS Berlin
- 7 more »
- « less
-
Field
-
, cutting edge research towards scalable quantum computing A research track record commensurate with our ambition Our Offer: We work on the very latest issues that impact our society and are offering you the
-
good publication track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline
-
19.09.2023, Wissenschaftliches Personal The Bienert Lab is part of the TUM School of Life Sciences of the Technical University of Munich located in Freising-Weihenstephan. The main objective
-
research and take over a leadership role (Team Lead) in the institute Motivation Do you want to put your scientific career on the fast track and feel electrified? Do you have ambitions to lead a research
-
– 57k Euro / year + benefits). Topics include: Neural Rendering, 3D Reconstruction, SLAM / Pose Tracking, Semantic Scene Understanding, Face/Body Tracking, Non-Linear Optimization, Media Forensics / Fake
-
ecologist (m/f/d) with a background in community ecology, macroecology, metacommunities, and/or analysis of ecological time series. The applicant should have a strong track record of scientific publications
-
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
-
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
-
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
-
consortium-based tasks related to the 6G-Life project. Additionally, the methods and findings developed throughout the PhD track will be scalable and applicable to other research projects in MIRMI