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-edge research at the interface of operations research, optimization, and machine learning, with a focus on quantum optimization . Background in discrete and non-linear optimization is preferred. The term
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performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the
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automated finite element and inverse-identification workflows that learn constitutive parameters from a single informative test through Bayesian optimization, together with quantification of measurement
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to a research direction on personalization and optimization of neuromuscular electrical stimulation. The position includes support to develop additional research and pursue independent funding within
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, access narratives, and strategic recommendations that support reimbursement, coverage optimization, and differentiated product positioning Create educational materials and resources that communicate