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laboratory and will focus on macromolecular engineering, synthesis and multi-scale/physics characterizations of structure/ionic transport properties of self-healing polymer electrolytes. In close collaboration
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 11 hours ago
NASA makes way for smaller scale missions, readily available, low SWAP instruments to deliver routine science will become more valuable. JPL requires development and testing of highly miniature control
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 11 hours ago
9-channel multi-angle polarimeter. The combined capabilities from these two instruments will provide unprecedented characterization of aerosol optical and physical properties, and their vertical
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general administrative support to the wider Registry team. Further details about the Registry can be found on our website: https://www.lshtm.ac.uk/study/studentservices/registry-services You should be
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The opportunity The University of Liverpool is a key partner in a £14 million initiative ( https://tinyurl.com/yc5z768m ) to develop a sustainable, next-generation manufacturing facility, using
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optical image, visual optics, and bio-medical implants. The Centre brings together a multi-disciplinary team of over 20 PhD and Post-Doc researchers in manufacturing engineering, instrumentation and optics
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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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Quantifying the distribution of the energy flux through the solar atmosphere generated by ubiquitous multi-scale vortical plasma motions School of Electrical and Electronic Engineering PhD Research
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within the framework of project MULTI-COAT - MULTIscale Coating Optimization with Artificial intelligence Technologies, with number 15050 and operation code at the Funds Platform COMPETE2030-FEDER-00870200