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to sectors such as Geographic Information Systems (GIS), mobility management, the Internet of Things (IoT), and industrial sensorization. Tasks will include: Definition of reusable models and components
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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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Fluid Dynamics simulation code developed by our Project Partners at the Barcelona Supercomputing Center. The PDRA will improve and validate an existing model we have developed to simulate analogue dyke
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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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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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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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”) 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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(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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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