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Particle Acceleration is looking for a PhD Student (f/m/d) Multimodal Reconstruction of Laser-Electron Accelerator Phase Space using Physics-Informed Deep Learning. Your tasks Understand the physical process
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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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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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impact the safety of flight. The thesis shall develop robust state estimation methods by combining factor graph-based sensor fusion, variance component analysis, and modern deep learning approaches such as
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learning (e.g. PINNs, PIGNNs) that predict network-level quality metrics such as RSSI and SINR, given the geometry, placement, and orientation of base stations and meta-surfaces in an urban environment
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Nanoscience: Synthesis of Carbon-Nanostructures on Inert Surfaces Biophysics: Learning
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or comparable with deep knowledge in continuum mechanics, structural analysis, advanced mathematics, modelling and simulation We are looking for first-class graduates with expertise in the RTG
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to study the behavior of waste forms and the performance of geotechnical barriers in deep geological repositories for high‑level radioactive waste. We investigate the release, migration, and retention