Sort by
Refine Your Search
-
Listed
-
Employer
- Forschungszentrum Jülich
- Technical University of Munich
- Nature Careers
- Leibniz
- Brandenburgische Technische Universität Cottbus
- Fraunhofer-Gesellschaft
- Academic Europe
- DAAD
- FH Münster
- European Magnetism Association EMA
- GFZ Helmholtz-Zentrum für Geoforschung
- Goethe University Frankfurt
- Goethe-Universität Frankfurt am Main
- Heinrich-Heine-Universität Düsseldorf
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Helmholtz-Zentrum für Infektionsforschung GmbH
- Karlsruher Institut für Technologie (KIT)
- LEUPHANA UNIVERSITY OF LUENEBURG
- Leibniz Universität Hannover
- Max Planck Institute for Demographic Research (MPIDR)
- Technische Hochschule Köln
- University of Cologne
- University of Göttingen
- University of Oldenburg
- University of Tübingen
- 15 more »
- « less
-
Field
-
systems. We intend to develop ground motion models that integrate large-scale databases of observed ground motions, physics-based simulations of seismic waveforms and cutting-edge machine learning methods
-
research experience, preferably in programming languages, compilers, applied mathematics, and optimization techniques a strong background in compiler, code generation, and machine learning would be
-
robotic system design, humanoid robot development, manipulation and bimanual manipulation, control, or machine learning for robotics. Excellent project management skills to meet and achieve the expected
-
in medicine and other study programmes and graduate initiatives in which the Faculty of Medicine is involved is expected. Beyond the core research area, the appointee will also teach in all degree
-
areas best match your interests. You would like to learn more about our institute? Get to know the Fraunhofer ITWM and gain insights into our Institute. (youtube.com) What you will do At our institute
-
ENGAGE Network at TUM and GIM Robotics. About the ENGAGE Network Mobile working machines (MWM) are critical to industries like construction, mining, and agriculture, and key to Europe’s sustainability and
-
of reaching climate neutrality by 2050. By leveraging characterisation, multiscale modelling, machine learning, and numerical optimisation, the project seeks to craft a magnetic multiscale modelling suite
-
machine learning (data-driven modeling, machine-learned force fields (MLFF), structure-property relationships) for the evaluation of large data sets and to generate new research approaches. In teaching, the
-
the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration
-
vision models Experience with event-based cameras, neuromorphic vision concepts, spiking neural networks, and/or neuromorphic computing is a plus Experience with, or willingness to learn, ROS 2 for robotic