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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
-body or highly nonlinear systems. The emphasis will be on how AI methods can both characterize and optimize trajectories within complex design spaces. Within this context, contingency planning
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on the ONR Project “Optimal Transport-based Strategies in Waves and Dynamics” led by Prof. Christina Frederick at NJIT. Essential functions will include research into optimal transport theory to capture
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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, supply chain resilience, and lifecycle sustainability. The successful candidate will lead and conduct independent research in optimization, systems modeling, agent-based modeling (ABM), and network
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to research projects, both independently and collaboratively. Sets up, maintains, and optimizes experimental equipment. Mentors and trains lab members in techniques and experimental design. Presents research
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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. Job tasks include optimizing intrinsic labeling systems (13CO2, 2H, 57Fe, etc.); developing high-throughput extraction, sample clean-up, and analysis methods for various plant foods and crops; analyzing
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probes. The postdoc will play a key role in designing and optimizing imaging devices, integrating hardware and software, and collaborating with a multidisciplinary team of clinicians, engineers, and basic
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of polyurethane materials in a self-driving laboratory. The successful candidate will work primarily on understanding the structure-property relationships of polymeric materials with a focus on optimization of
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning