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: 267826999 Position: Postdoctoral Research Associate Description: The Program in Latin American Studies (PLAS) is seeking candidates from any discipline who are engaged in scholarly research on topics related
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the intellectual life of the Program in Linguistics. A PhD in Linguistics or relevant discipline is required. The Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with
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emerging technologies such as artificial intelligence, quantum technologies, and space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer
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availability of funding. The anticipated start date for the position is June 1, 2025. Individuals with a strong theoretical background who expect to obtain a PhD in a related field (e.g., statistics
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boundary layer and apply them to ocean climate models. Our previous work demonstrated that neural networks can learn to predict the vertical structure of vertical diffusivity and the networks can then be
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, molecular biology, biochemistry, physics, computer science, and genetics. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending
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excellence in education are encouraged to apply. PhD is required. Applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/37861 and include curriculum vitae, research statement and names
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methodologies for modeling and analyzing spatially embedded networks. This project aims to advance the understanding of infrastructure systems by leveraging spatial networks to capture complex interdependencies
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space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer science, or other relevant fields and strong interest in technical and policy research
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation