113 machine-learning "https:" "https:" "https:" "https:" "https:" scholarships in Germany
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- Technical University of Munich
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- Brandenburg University of Technology Cottbus-Senftenberg •
- Giessen University
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- Deutsches Elektronen-Synchrotron DESY
- Deutsches Elektronen-Synchrotron DESY •
- GFZ Helmholtz-Zentrum für Geoforschung
- Helmholtz Zentrum Hereon
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Helmholtz-Zentrum Geesthacht
- Hertie School •
- Karlsruher Institut für Technologie (KIT)
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH
- Max Planck Institute for Biogeochemistry, Jena
- Max Planck Institute for Biological Intelligence •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Meteorology •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute for the Study of Societies •
- RPTU University of Kaiserslautern-Landau •
- Saarland University •
- Technische Universität Dresden
- University Hospital Jena
- University Hospital of Schleswig Holstein
- University of Bamberg •
- University of Potsdam •
- Universität Hamburg •
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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approaches are gaining importance for autonomous vehicles. However, the training and certification of autonomous systems with machine learning components is a huge challenge, since the learned behavior is
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Requirements: Applicants should hold an MSc or Diploma in Engineering, Computer Science or a related discipline. Background in Machine Learning and Artificial Intelligence. Strong programming skills (Python
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on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments
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with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine learning, and porous materials strong analytical and
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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developing a digital twin, employing machine learning and numerical computations of atomistic processes. At IKZ, a kinetic Monte Carlo tool has been developed in the programming language julia. This allows a
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more