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records from stalagmites which grew in coastal caves, to reconstruct the phasing of changes in the North Atlantic salinity relative to AMOC variations, and interpreting them with the aid of process models
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”) on the development of material flow analysis (MFA) methods and digital methods for spatial analysis. Specific tasks comprise: Design and apply digital models for analyzing and simulating circular futures
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learning workflows, and developing complete models. Example applications include drug design, cryo-electron microscopy, structural prediction and dynamic simulation of biological macromolecules, genomics
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of celestial sources observable with XRISM, simulations of potential XRISM observations, relevant laboratory astrophysics, development of spectral models or atomic codes, or exploring synergies between XRISM and
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. This technology is being developed principally for space applications, but we are also using this to support a vigorous program in laboratory astrophysics using an electron beam ion trap to simulate astrophysical
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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, the CSATLab , our SW Simulators , and our Facilities . For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/ . Your role Develop innovative methods and data-driven AI tools
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extensive electromagnetic modeling to optimize the waveguide structures for minimal loss, efficient confinement, and effective mode matching with external optical components. Particular attention will be
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analysis, performance evaluation, and liaison with local and industrial partners. Contribution to the development of digital tools, models, and algorithms related to the simulation, optimisation, and
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and