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. Adapt state of the art multicomponent, multiphase LBM models for performance on both CPU and GPU hardware. Identify new features and software capabilities to implement to support domain scientists. Author
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on insights gained from experiments. This position will provide access to state-of-the-art instrumentation available at NREL and networking and collaboration opportunities with a multi-institutional team
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. The successful candidate will be able to: • co-design the planning and execution of protein selection, design, and engineering efforts with other researchers • use state-of-the-art deep learning
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Enabling Sciences Center. The successful candidate will be able to: Develop and apply state-of-the-art computational tools to study rare events processes, particularly nucleation from solution Design and
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economically attractive, and more scalable than the state-of-the-art multi-layer plastic film recycling technologies. This position will work with a dynamic, multi-disciplinary experimental team and analysis
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and collaboratively as needed co-design the planning and execution of protein design efforts with other researchers use state-of-the-art deep learning methods to develop predictive models with