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Carnegie Mellon University, Institute for Computer-Aided Reasoning in Mathematics Position ID: 3637-PF [#27988] Position Title: Position Type: Postdoctoral Position Location: Pittsburgh
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models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation
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enables improved resilience as partitions of the grid can be independently powered. This PhD project aims to develop the methods needed to take advantage of this opportunity. This includes methods for pre
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of this Postdoc Fellow position include: Developing, testing, and analyzing research results through empirical and computation methods to support projects funded by federal agencies with background in metallurgy
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of this Postdoc Fellow position include: Developing, testing, and analyzing research results through empirical and computation methods to support projects funded by federal agencies with background in metallurgy
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the group can test and validate models of sodium-ion battery behaviour. As such, proficiency in computer programming (particularly python) is required as well as knowledge of numerical methods
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-based computed tomography, with numerical simulations informed by microstructural data. The successful candidate will work at the interface between experiments, modelling, and data-driven methods
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tomography, with numerical simulations informed by microstructural data. The successful candidate will work at the interface between experiments, modelling, and data-driven methods. Particular emphasis will be
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding