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of identified cell types and computational modeling. Thus we expect these circuit-level reconstructions to inform specific biological experiments, enabling dramatic leaps in our understanding of brain function
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zebrafish, mouse and fruit fly. Generate crucial data from EM images of above organisms, to train artificial intelligence models using custom software(s). Maintain high standards of consistency, precision
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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scientists to join the NOAA Research Global-Nest Initiative. This multi-laboratory project aims to develop ultra-high resolution atmospheric prediction models for better prediction, understanding, and
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. The scope of the work builds on recent publications from the laboratory, e.g., predicting future illicit drugs with chemical language models (https://www.nature.com/articles/s42256-021-00407-x ) and re
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practices uniquely appropriate for that project (e.g. version control, continuous integration and continuous delivery, software design, programming model, etc.) - Learn how to document projects in a
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methods range from process-tracing in controlled lab and virtual-lab experiments, to field experiments in real-world settings, to using big data, and include both quantitative modeling and qualitative
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H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering large AI models or their applications. Current PLI projects involve work with
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PRINCO Diligence teams and collaborate with legal counsel, helping to structure and enable new investments. • Develop financial models to evaluate alternative tax entity structures. • Engage with legal
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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and