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on heterogeneous processor types Optimized GPU computing and exploitation of GPU architectures for HPEC (tensors, multi-GPU instantiations, advances in GPU for AI/ML) Compute-focused optimization of System-on-Chip
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) VS Code (extensions for Python, Jupyter) Cloud Platforms & Remote Development Google Colab: Managing notebooks, using GPUs, data integration AWS / Azure / GCP basics (at least one of the three
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3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running
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released SecureData4Health (SD4H) OpenStack cloud infrastructure. It currently includes 15,000 VCPU, 60 Petabyte of storage, 30 GPU and is growing as additional academic research projects join. The Software
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data processing, as well as experience working with large biological datasets in GPU-based computing environments. What we provide: A competitive compensation package, with comprehensive health and
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, and Ising machines. The group possesses strong expertise in magnetic, electrical, and optical characterization techniques (particularly micro-focused Brillouin Light Scattering) and leverages GPU
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working with climate, weather and earth datasets formats (netcdf, zarr,..) . Working knowledge of High-performance computing (HPC). Experience with GPU-accelerated machine learning frameworks such as RAPIDS
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. Experience with GPUs and performance optimization of ML at scale. Familiarity with containerization and HPC workflows. Notes: This is a full-time, career appointment, exempt (monthly paid) from overtime pay
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likely to be required include advanced Python or c++ with experience in developing and fine-tuning foundation models for modelling tasks, including use of HPC systems and multi-GPU programming. Knowledge
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to benefit from latest advances in hardware technologies the ICON model was ported to run on Graphics Processing Units (GPUs) and is one of the first model that can be used in production