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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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: 270175784 Position: Postdoctoral Research Associate Description: Position Title: Postdoctoral Research Associate in the Cognitive Science of Values The Department of Psychology, in collaboration with
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. Essential qualifications for this position include: a Ph.D. in Neuroscience, Psychology, Cognitive Science, Computer Science, Engineering, or other related field, and strong experience with computational
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& Perception Laboratory (NA&P Lab), led by Dr. Sabine Kastner at the Princeton Neuroscience Institute. The lab studies neural mechanisms of cognition in the primate brain. Intracranial recordings from human
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network analysis libraries and relevant modeling tools, are necessary. Knowledge of spatial data analysis, graph theory, and infrastructure systems will be viewed favorably. Exceptional analytical, problem
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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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Position Title: Postdoctoral Research Associate in the Cognitive Science of Values The Department of Psychology, in collaboration with the University Center for Human Values, invites applications
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include: a Ph.D. in Neuroscience, Psychology, Cognitive Science, Computer Science, Engineering, or other related field, and strong experience with computational models, programming, and quantitative methods
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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