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the problem is explicitly considered. In particular, it will investigate how to tightly integrate state-of-the-art sampling-based methods with state-of-the-art methods from numerical optimal control in a
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of numerical precision/instabilities. Different physical configurations of NEMO, either local or global, will be studied. We also plan to optimize the threshold values in NEMO, taking into account the various
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experience developing biological application an additional plus Experience coding / applying finite difference, finite element or finite volume methods Experience using optimization software such as GAMS
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. We leverage advanced technologies like semantic data processing, signal processing, and network resource management to enhance performance. To optimize and analyze complex 6G networks, we use AI/ML
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different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover missing links in a multilayer network with connected
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Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms for smart energy systems to enable smart and healthy
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possible until 31 December 2027. Key responsibilities and duties: Use analytical and numerical mathematical tools to design a sensor system using infrasound and ultrasound. Propose different sensor systems
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of PFR measures, and are now seeking a suitably qualified postdoctoral researcher with a desire to make a difference in the real world. The successful candidate will establish and deliver an active
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or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover
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Post-doctorate position (M/F) : Exascale Port of a 3D Sparse PIC Simulation Code for Plasma Modeling
to exascale architectures an initial 3D simulation code developed as part of previous work [1]. This work will initially focus on scaling up (distributed memory), optimizing CPU algorithms (vectorization) and