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Field
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application of machine-learning pipelines Strong knowledge of software frameworks for implementing GPU-accelerated computations, in particular JAX and PyTorch Knowledge of HPC environments and experience in
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sciences Strong foundation in bioinformatics and machine learning, including deep learning Experience training and running models on shared HPC clusters and remote GPU servers, including working in multi
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. Experience or training and deploying models on Graphics Processing Unit ( GPU ) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 24 days ago
, interdisciplinary 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
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( ABD ) status will also be considered (Degree Conferral in Process) Certifications/Licenses Required Knowledge, Skills, and Abilities Experience in GPU programming Experience working in interdisciplinary
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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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) Experience managing GPU-enabled cluster nodes Desirable: Experience working with current large language models (LLMs) and their ecosystems Required Application Materials: Cover letter that includes a
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Theory Center (CatTheory), which provides a strong infrastructure and computational facilities, including the Niflheim supercomputer with GPU nodes at DTU Physics. Responsibilities and qualifications The
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quantitative data analysis, statistical reasoning, validation and interpretation of results. Experience with FPGA, GPU or accelerator technologies. Familiarity with high performance or embedded
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and graduate research assistants, and presenting at design reviews and project milestones. Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high-performance GPU