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Field
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 19 days ago
with rotorcraft aerodynamics, comprehensive analysis tools, or high-order unstructured solvers Experience with GPU-accelerated CFD workflows Point of Contact Mikeala Eligibility Requirements Degree
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. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence
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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage
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Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems or edge
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of whole rodent brains; memory-efficient I/O, tile-based or chunked processing, multi-resolution formats such as OME-Zarr) and GPU computing on cloud or HPC environments. Experience mentoring or training
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 18 hours ago
approaches for automated medical devices (e.g., physiologic closed-loop controlled devices). Developing multi-spectral computational modeling tools using GPU-based processors to map light propagation
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems