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conduct research on the use of machine learning in ocean climate models. The goal is to demonstrate the successful use of machine learned parameterizations of unresolved processes that will reduce biases in
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conduct research on developing and using machine learned parameterizations for mixing in the ocean surface boundary layer. Our previous work has demonstrated the utility of using neural networks to improve
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conduct research on developing and using machine learned parameterizations for mixing in the ocean surface boundary layer. Our previous work has demonstrated the utility of using neural networks to improve
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approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a range of exciting projects
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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting September 2023, and will remain open until excellent fits are found. Successful candidates
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The Princeton Computational Imaging Lab in the Department of Computer Science explores the frontiers of imaging and computer vision. To develop next-generation imaging and vision systems, the lab
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with a pick-and-place robot guided by computer vision. The task of the robot is to pick up slices floating on water and place them on substrates suitable for electron microscopic imaging. This is the
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with a pick-and-place robot guided by computer vision. The task of the robot is to pick up slices floating on water and place them on substrates suitable for electron microscopic imaging. This is the
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enhancing software developed in the lab including MAVEN and NET-ID, (iii) contributing to computer-guided discovery of novel metabolites, (iv) establishing software for imaging metabolomics, (v) carrying out
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The Joseph Research Group is searching for postdoctoral or more senior research candidates interested in computer simulation studies of intracellular organization, biomolecular self-assembly, and