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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 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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physics, 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
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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, PyTorch) for ML applications, training, evaluation, and deployment of models Use of GPU-based servers and modern IT infrastructure for training and inference Application of classical ML methods (e.g
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, including Large Language Models (LLMs), agent-based systems, and Retrieval-Augmented Generation (RAG). Practical expertise in training and optimizing neural networks on high-performance (GPU-enabled
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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
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degree in physics, 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
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient