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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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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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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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of future immersive learning opportunities. Your tasks Technical development and implementation of a VR-based learning environment Integration of AI-driven learning modules Creation of industry-specific
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wide range of theoretical perspectives, methodological approaches, and links to educational practice. The interdisciplinary course program focuses on the processes and outcomes of teaching and learning
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers two full-time positions as Research Associate / PhD Student (m/f/x
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Description For our location in Hamburg we are seeking: Doctoral Researcher in Machine Learning and Data Processing in the Field of Seismic Measurements Remuneration Group 13 | Limited: 3 years
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Helmut-Schmidt-Programme (Master’s Scholarships for Public Policy and Good Governance - PPGG) • DAAD
list of countries), who want to promote democracy and social justice in their home countries. The programme, which is funded by the German Federal Foreign Office, offers the chance to acquire a Master’s
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data