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(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative
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candidates working in formal methods and/or computer systems with interests in one or more of the following areas: machine-checked verification of systems software, GPU kernel semantics and verification, and
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systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python, using modern practices for software development, testing, documentation, and version control
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 14 days ago
group. Our offer Computing The department operates Urania, a high-performance cluster with ~6,050 cores; Hypatia, a high-throughput cluster with ~12,000 cores; and two GPU servers, Saraswati and Lakshmi
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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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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 8 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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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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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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extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA, PyTorch, or GPU-enabled LAMMPS). ● Experience
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applications for scientific, agricultural, environmental, geospatial, or cyber-physical systems. Experience with GPU computing, distributed training or inference, high-performance computing systems, cloud-native