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optimization, scale-up, purification, and preparation of well-characterized target compounds. Apply a range of analytical and characterization techniques to evaluate chelator structure, affinity, selectivity
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, optomechanics, optical cavities, and/or photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of
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and conferences. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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learning, optimization, scientific computing, domain sciences, and high-performance computing. The candidate will have opportunities to work with world-class computing resources, including ORNL’s
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other geometric deep learning approaches for graph-structured scientific or engineering data. Background in electrical engineering, power systems, grid modeling, or power system optimization
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expertise in multi-physics FE simulations is preferred. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or
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the development and optimization of products for a variety of industries from automotive to aerospace made from new bio- and waste-derived plastic resins and fillers. The ideal candidate for this
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validation. Experience or strong interest in scientific AI/ML, including surrogate or multi-fidelity modeling, digital twins, generative models, adaptive optimization, or autonomous experiment–simulation
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expertise in multi-physics FE simulations is preferred. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process
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interfaces and protocols, such as Address-Event Representation (AER). Experience with hardware-aware AI optimization, including quantization, pruning, fixed-point arithmetic, and memory-footprint