Sort by
Refine Your Search
-
Listed
-
Employer
- Fraunhofer-Gesellschaft
- Technical University of Munich
- Nature Careers
- Leibniz
- Forschungszentrum Jülich
- University of Tübingen
- Free University of Berlin
- DAAD
- Technische Universität München
- Heidelberg University
- Universität Hamburg
- Academic Europe
- European Magnetism Association EMA
- Georg August University of Göttingen
- German Cancer Research Center
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Katholische Universität Eichstätt-Ingolstadt
- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute for Demographic Research (MPIDR)
- Max Planck Institute for Demographic Research, Rostock
- Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Max Planck Institute of Molecular Cell Biology and Genetics
- RWTH Aachen University
- Technische Universität Ilmenau
- The Hamburg Centre for Ultrafast Imaging (CUI), Cluster of Excellence
- University of Bayreuth
- 17 more »
- « less
-
Field
-
optimization Close collaboration with synchrotron groups and in-house scientists for design, commissioning, and operation of experiments Collaboration with the INW-1 machine learning team on data handling
-
are developing AI that can map welding processes in the automotive industry based on simulation data. We are looking for a student assistant with an interest in machine learning and finite element simulation. Your
-
and/or AI-based approaches), including the integration of concepts such as reinforcement learning and multi-agent reinforcement learning Further development and use of virtual test environments
-
for industry. To this end, the latest findings from the fields of artificial intelligence, machine learning and cloud-based methods are combined with proven expert knowledge to answer current questions in robot
-
manufacturing and laser material processing. We are currently developing machine learning-based approaches to make the laser powder bed fusion (LPBF) process more efficient and improve its quality. To this end, a
-
applications. Our overarching aim is to obtain a holistic view of interconnected biological systems in health and disease. We develop clearing technologies for cellular-level imaging and deep learning algorithms
-
contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular behaviors and gene
-
multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant
-
Research Back Profile Areas Cluster of Excellence CMFI Cluster of Excellence GreenRobust Cluster of Excellence HUMAN ORIGINS Cluster of Excellence iFIT Cluster of Excellence Machine Learning Cluster
-
neural simulators (NEST, Brian, etc.) and/or machine learning frameworks (PyTorch, Tensorflow, etc.) is a plus Experience with spiking neural networks and/or neuromorphic computing is a plus Please feel