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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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the climate crisis and create an environmentally sound future for generations to come. To learn more about SEAS and our values, please visit our website at https://seas.umich.edu/about/seas-values. Why Work
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IOCB Prague / Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences | Czech | 3 months ago
for cryo-EM data processing and analysis at the local parallel computing cluster (400 CPU nodes, 87 GPU nodes, ~3 PBs of storage space and 1 PB of scratches). The successful candidate will be a key and
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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management, high-performance computing systems, GPU acceleration, and parallel file systems * Documented experience with container and cloud technologies such as Docker, Helm, and Kubernetes * Ability
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Engineer will support the development and operation of the GPU-accelerated, real-time data analysis pipelines that will turn images from the world’s largest digital camera into discoveries. Three years
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of multimodal communication. To do so, you will have full access to motion-capture and virtual-reality labs, 3D animation tools, and GPU-based high-performance computing at MPI. You will also be embedded in a
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and high flexibility in where and when you work. Access to HPC resources (including GPU clusters) at Helmholtz, the Leibniz Supercomputing Centre (LRZ), and the Forschungszentrum Jülich (FZJ). Training