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). Experience training AI models on GPUs. High motivation for research and a commitment to publishing at top conferences. Proven experience in submitting research to top-tier venues, such as ECCV, CVPR
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(URCF) at Drexel University is building a new shared computing platform focused on GPU-accelerated workloads, particularly AI model training. The system includes GPU and CPU compute nodes with Nvidia H200
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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experiments, particularly ATLAS and DUNE. Contribute to the architecture and core development of the Phlex framework, emphasizing scalable, multi-threaded, and heterogeneous (CPU/GPU) computing models
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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• Execute large-scale simulations on CPU and GPU-based HPC clusters • Analyze results, generate technical reports, and deliver project outcomes on schedule • Prepare scientific reports and publish in
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
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, Cloud Service Deployment). Desired: Experience with High-Performance Computing or GPU programming (CUDA). Specialized knowledge of Neural Rendering (NeRF/3DGS) or Satellite Photogrammetry. Demonstrated
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and collaborative team of computational scientists, software and AI engineers, and neuroscientists, you’ll have access to high-performance workstations, CPU/GPU clusters, and experimental systems