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implementation and applications in other scientific and engineering domains. Job description To reliably use simulation-generated predictions in science and engineering, one needs trustworthy mathematical models
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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computational models to capture the Multiphysics behavior— including fluid flow, heat transfer, and reaction dynamics—governing the co-precipitation process. Leveraging the ASCC supercomputer at UM6P, three
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Sequestration by demonstrating at least some of the following competencies. Experimental development, testing, and demonstration of novel CCUS technologies Process simulation, cost, life cycle, and social
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 14 days ago
of subgrid models in simulations, in particular for feedback processes Creation of new reference cosmological simulation models of galaxy formation Quantification of modelling uncertainties in simulation
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-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios. Integrate battery
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fabrication process, which does not need to be modified. The electrical behavior of these crossings will then be studied through electromagnetic simulations and optimized to achieve the best possible
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. Experience with modelling and simulation of stochastic processes, including birth-death processes is also required. The post is available fixed-term until 31 March 2027, funded by the Medical Research Council
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Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? Yes Offer Description Deadline for applications: 08/01/2026 Application process open
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agentic architectures for autonomous scientific reasoning and planning; • AI social scientists, including language-model-based and agent-based simulations for social science domains such as history