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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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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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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
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or more senior research positions. Successful applicants will join Princeton's Net-Zero X (NZx) initiative, which is building on the impactful Net-Zero America (NZA) study (https
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researchers or more senior research positions. Successful applicants will join Princeton's Net-Zero X (NZx) initiative, which is building on the impactful Net-Zero America (NZA) study (https
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attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
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collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical publications. The candidate will be appointed in the Department of Psychology
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values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical publications. The candidate will be appointed in the Department
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. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel Daw, ndaw@princeton.edu. Review of applications
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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