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program aims to fundamentally advance simulation methods and consolidate novel data to understand how systemic physical climate risks emerge in the socio-economic system, and to explore strategies
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 13 days ago
Job Offer from November 03, 2025 The Max Planck Institute for Gravitational Physics (Albert Einstein Institute, AEI) in Potsdam, Germany, announces the opening of several PhD positions at the IMPRS
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Three PhD positions at TU/e in collaboration with Canon Production Printing, combining polymer science, rheology, and computational modeling to create the inks and coatings of tomorrow. Information
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architected materials or metamaterials (MTM) that can undergo targeted non-linear response. You will develop a computational framework that can reveal novel Multiphysics (thermo-mechanical) MTM solutions
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physics-based models for circuit simulations. The characterization and modelling work will be in strong collaboration with a second Ph.D. student within the same project, who develops ultralow-power circuit
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laboratories, among which are UU’s Geolab , and the Earth Simulation Lab . We also have excellent High-Performance Computing facilities. At EMBRACER we work at the very frontiers of knowledge on climate change
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies
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11 Nov 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Electrical engineering Engineering » Process engineering Researcher Profile
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highly motivated researcher with a strong interest in electron optics, instrumentation development, and computational modelling, eager to bridge simulation and experiment to realize next-generation
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning