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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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time scales. The core responsibilities of the Research Specialist will include: expand/develop code in R for conducting multivariate statistics and deep learning analyses using discrete and continuous
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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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learning opportunities, post-graduation employment and graduate/professional school planning in both individual and group sessions • Help students clarify their values, interests, skills, and strengths and
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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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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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minimum of five years relevant professional experience in an academic or research library, or the equivalent, which must include a minimum of three years of supervisory and management experience Deep
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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