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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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processing and inversion techniques to experimental data from different regions and link the findings to relevant processes of the soil-plant system. For further information visit our website http://www.fz
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on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training and optimizing the execution User support in
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build reliable, reproducible data flows for large EO datasets and workflows Lead performance engineering (parallelization, optimization, benchmarking) for adaptation and inference at scale Work closely
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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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process, the role of ISOs passing through molecular clouds, taking part in molecular cloud collapse and disc formation. Your tasks in detail: Perform scientific work on the research topic, in collaboration