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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 13 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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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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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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act as the host at TUM. Possible topics of research include, but are not limited to • Exploiting GPUs for large-scale optimization • Value stacking of storage in competitive wholesale electricity
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act as the host at TUM. Possible topics of research include, but are not limited to • Exploiting GPUs for large-scale optimization • Value stacking of storage in competitive wholesale electricity
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will be a dedicated project to prepare, optimize, and benchmark the code
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architectures. Emphasis will be placed on heterogeneous computing and the optimal use of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will
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collation, mixed precision, multi-GPU; experiment tracking (W&B or MLflow), configuration management (Hydra), strict seed and artefact reproducibility. Graph machine learning. PyTorch Geometric or DGL
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baselines Solid Linux experience in production environments (RHEL/Almalinux/Ubuntu) Hands-on HPC background: Slurm, parallel file systems (Weka, Lustre, Ceph), GPU workloads and high-speed networks