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finite-element models, e.g. Poisson, linear elasticity, large-deformation soft tissue, for real-time execution on AR devices and GPUs Implement these models within open-source frameworks such as SOFA
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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
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for accelerators, such as GPUs or FPGAs. Experience in refactoring or porting large codebases (over 100k source lines of code). Background in supporting scientific code on HPC systems or familiarity with components
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer
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program embedded in a large-scale, nationally funded research consortium with access to unique multimodal clinical datasets - State-of-the-art GPU infrastructure for training and fine-tuning large
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. Knowledge of GPU architectures, GPU cloud computing services, and strong familiarity with Linux operating systems. Knowledge of BIM, universal scene description and scene composition Knowledge of physics and
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
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-term project. We are looking for a software engineer to develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU