Sort by
Refine Your Search
-
Category
-
Employer
- DAAD
- Technical University of Munich
- Nature Careers
- Fraunhofer-Gesellschaft
- University of Tübingen
- Brandenburg University of Technology Cottbus-Senftenberg •
- Leibniz
- University of Münster •
- Deutsches Elektronen-Synchrotron DESY •
- Forschungszentrum Jülich
- Hannover Medical School •
- Helmholtz-Zentrum Geesthacht
- Hertie School •
- Karlsruhe Institute of Technology •
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Biogeochemistry, Jena
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Meteorology •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute of Biochemistry •
- Max Planck Institutes
- Technical University of Darmstadt •
- Technische Universität Berlin •
- University of Bremen •
- University of Göttingen •
- University of Potsdam •
- 16 more »
- « less
-
Field
-
, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
-
mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. TUD has established the Collaborative
-
the field of biotechnology, bio-/chemical engineering, (bio) process engineering, bioinformatics, biophysics or biomathematics. Ideally you have Programming skills and knowledge on machine learning and
-
therefore teams up materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH ( AMO ) in Aachen, Forschungszentrum Jülich
-
breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
-
, mechanical engineering, physics or similar basic programming skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g
-
models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
-
/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
-
reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl