20 communication-network-optimization PhD positions at Forschungszentrum Jülich in Germany
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Identify suitable application task in the field of geolocation and optimize network and learning rules accordingly Design, set up, and operate experimental systems for circuit-level measurements and data
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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, optimize and extend existing setups, develop a novel setup to perform an absolute radiometric calibration Perform a line-of-sight calibration to precisely align the instrument’s viewing geometry with
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project, you will help design, simulate, and optimize these next-generation communities — making clean, local, and intelligent energy systems a practical reality. Your key responsibilities include
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parameters Development of learning rules considering the strong non-linearities of the neurons Identify suitable application task in the field of geolocation and optimize network and learning rules accordingly
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the inductive heating of heterogeneous catalysts as an integral advantage. Your focus will be on the investigation, development, and optimization of this new process in a continuously operated reaction
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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energy system optimization models including, e.g., reservoir productivity predictions, novel surface processes for CRM extraction, CO₂ reinjection, and reconversion of decommissioned oil wells Economic
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. Responsibilities and tasks This PhD project aims to optimize the design of hybrid electrical–optical computing architectures: Investigate and design optimal computing and communication architectures for hardware
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing