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Field
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Conduct physical vapor deposition experiments using advanced PVD processes such as synchronized HiPIMS Perform in-depth materials characterization and surface analysis Apply and improve high-throughput
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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, Artificial Neural Networks, and neural decoding, including cross-condition generalisation and coding similarity analyses of MEG activity patterns. The project is closely articulated with a PhD project in the
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University of North Carolina at Greensboro | Greensboro, Indiana | United States | about 17 hours ago
institution in North Carolina for net cost by the N.Y. Times and No. 1 in North Carolina for social mobility by The Wall Street Journal — helping first-generation and lower-income students find paths
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develop and become more severe, and as they improve with treatment conditions. These studies aim to provide the rationale for development of neuromodulatory approaches designed to reshape network activity
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to the ELLIS network. Your work will benefit from interactions with computational, biological, and clinical groups across the institute, alongside opportunities for international collaboration, research
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, analysis software, and system layout for next-generation neutrinoless double-beta decay experiments. We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play
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framework, collaborate across multidisciplinary networks (biologists, clinicians, materials scientists, and pharmaceutical researchers), and mentor PhD candidates and junior researchers. Where to apply
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is initiated. Experience in electrochemistry, materials synthesis and characterization. Strong expertise in OER/ORR: performance and product analysis. Ability to cooperate with experimental teams
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interdisciplinary research program. The project also provides excellent opportunities to develop expertise in AI for biodiversity science while building a strong publication record and collaborative network