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learning architectures for scientific or high-performance computing applications. Background in software performance evaluation, profiling, and optimization on CPUs and GPUs. Knowledge of common numerical
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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regulations related to health care Attention to detail and accuracy Computer literacy Preferred Qualifications Experience and demonstrated skill with using the teaching method of asking questions for self
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experience Demonstrated programming expertise in MATLAB and/or Python (object-oriented design, numerical methods, scientific visualization) Prior experience in scientific computing or within the subsurface
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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five years. Demonstrated expertise in computational mechanics and numerical modeling Experience in polymer composite manufacturing processes Experience with simulation tools for thermomechanical analysis
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain