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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
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of the host organization of last 36 months. As secondments and events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu
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are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en
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research activities are organized around three main areas—Combustion, Non-equilibrium Plasmas, and Transfer Physics—along with a cross-disciplinary program in Applied Mathematics. Where to apply Website
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must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_la… ) LanguagesENGLISHLevelExcellent
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Physics and AI: Physics-Informed Machine Learning (PIML): Physics-informed neural networks, with applications to Fluid dynamics, plasma physics, elasticity, weather modeling AI for Scientific Discovery in
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), Plasma etching (RIE) and wet etching processes, Electron microscopy , III-N semiconductors, cathodoluminescence Other skills: English written/spoken Website for additional job details https
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. To be successful in the role you will: Have a relevant PhD or equivalent research experience in electrical engineering, pulsed power, plasma physics, electromagnetic science/engineering or other relevant
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Developing state-of-the-art Space Weather forecast tools School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof Robertus von Fay-Siebenburgen Application Deadline
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techniques to model fluid turbulence, fusion plasmas (with a particular focus on inertial confinement fusion target design), and quantum circuit simulators. The work will range from algorithmic and theoretical