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Field
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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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analysis systems using GPU- and FPGA-supported HPC clusters at large international research facilities such as Effelsberg, SKA, and MeerKAT. The systems developed by the BDG are based on state-of-the-art
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international journals or conferences. Experience in predictive modeling for forecasting or recommendation systems. Strong programming skills in Python and AI frameworks (PyTorch, TensorFlow), including GPU/cloud
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond)
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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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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | 25 days ago
Description Analysis and development of methodologies to accelerate the computation of numerical optimization through parallelization and the use of GPUs. Where to apply Website http://www.unibo.it Requirements
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frameworks (PyTorch, TensorFlow). Experience with dataset curation, annotation workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data
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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
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model in collaboration with partner institutions such as the German Climate Computing Center (DKRZ) and German Weather Service (DWD), including GPU porting. They will perform production runs of ICON and
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frameworks). Experience using open-source model ecosystems such as Hugging Face (Transformers, Datasets, Accelerate). Experience using or supporting supercomputing or GPU-enabled clusters. Experience with data