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Field
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laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
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area of High Performance Computing, Machine Learning (due to substantial investment in high-end GPUs) and Containerisation. About the Department The Research Division of IT Services provide the computing
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 6 days ago
, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the above-mentioned fields. What we offer State-of-the-art on-site high performance/GPU compute facilities
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environment and legal frameworks. Procure consumables and research equipment and research resources eg GPU time Project operations: Coordinate the Project’s workplan, governance processes, meetings, records
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experience. Assist researchers in areas like A.I. and machine learning by recommending, configuring, and maintaining GPU servers/hardware. Collaborate with faculty and research groups to design computing
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specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National High-performance Computing Center. ASIAA is
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experiments. You will experience designing and operating massive-scale GPU and combined CPU/GPU workloads across these services. You will design and debug platforms, and will work closely with researchers as
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managed as software-defined infrastructures, and integrated as complete computational experiments. You will experience designing and operating massive-scale GPU and combined CPU/GPU workloads across
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Dask and NetCDF or Zarr datasets. Experience with GPU or distributed training and familiarity with agricultural or environmental applications of weather forecasts. Experience working in interdisciplinary
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with scientific-data tools and formats such as xarray, Dask, NetCDF, or Zarr. Experience with GPU computing, distributed training, high-performance computing, or cloud computing. Experience with