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in R for conducting multivariate statistics and deep learning analyses using discrete and continuous phenotypic data; Assist lab members on conducting Python-based Bayesian inference analysis
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and the Environment invites applications for postdoctoral researchers or more senior research positions. Individuals interested in systems-level analysis of challenges to deep decarbonization
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data analysis and modeling expertise across this interdisciplinary research space at Princeton. Data scientists should have a strong track record in modeling big data using advanced deep-learning methods
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commitment of at least two years is preferred. Responsibilities Quantitative analysis of fluorescence signals, including automatic cell recognition using deep learning and custom Python scripts. Operating
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University's educational mission and its desire to serve society. Members of the Geosciences department have a deep commitment to being inclusive. We believe that commitment to principles of fairness and
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, methodological rigor, and deep regional knowledge of China (including the ability to read, write, and speak Chinese). The successful candidate will be jointly appointed in a social science or related department
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of cutting-edge data analysis pipelines that use deep learning to reconstruct perceived information from functional MRI in real time. They will also have the opportunity to assist with the design, programming
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the lab of Professor Ken Norman. The Research Specialist will assist with the design and implementation of cutting-edge data analysis pipelines that use deep learning to reconstruct perceived information
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) Realistic energy transition pathways: Incorporate political economy insights into energy system modeling to better represent social and political realities; (2) Health effects of deep decarbonization
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of methodological rigor and clear records of upcoming and completed tasks. - Use existing software and deep learning tools developed in the lab to facilitate large-scale data analyses. - Apply developed data