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reconstruction, and support NOVO staff working on numerical aspects with experimental insights. The candidate will collaborate with national and international experts on detector development, electronics and
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of robotics Solid mathematical foundation paired with practical robotics experience Strong programming skills in (at least one of) Python/C++ Familiarity with robotics frameworks like ROS; optimization-based
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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optimize and ex-ante evaluate different components of an architecture for improving global public good provision that is sketched here . The architecture consists of two components, tax clubs and reward
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protocols of interest are 61, 62 (bioburden testing), 71 (sterility testing), and 85 (bacterial endotoxin testing). The researchers will be asked to optimize lab-specific protocols and run the protocols at a
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digital twins to develop innovative solutions for monitoring, analyzing, and optimizing urban systems in real time. The candidate will contribute to modeling interactions between physical and digital
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to optimize lab-specific protocols and run the protocols at a larger scale for prototype testing. The candidate is also expected to perform the biological testing detailed in the aforementioned protocols in
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access to state-of-the-art numerical models and high-performance computing systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and
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the problem is explicitly considered. In particular, it will investigate how to tightly integrate state-of-the-art sampling-based methods with state-of-the-art methods from numerical optimal control in a