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quantum networks” (TV-L E13) (f/m/x) for the project "6G and Quantum Technology (6GQT)", which is being funded by the Bavarian State Ministry of Economic Affairs, Regional Development and Energy. ). The
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on Compact Manifolds with Novel Feedback Coupling and Complex Network Optimization." Specifically, the selected candidate will address Research Objective (RO) (2) Investigate the dynamical phenomena of orbital
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of fluorescent biological sensors, DNA/protein purification, mouse survival surgery, two-photon microscopy and analysis, patch-clamp recording, single-cell electroporation, software development, optogenetic
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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
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undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected
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conducting qualitative network analyses of the connections between land use, water quantity and quality, marine food webs and species, governance, and ecological, social and economic system dynamics and
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automation, machine learning, mobile robotics, process control, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners, corporations, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
developing software and analytic tools to perform disease modeling and critical analytics in response to infectious disease outbreaks. ACCIDDA is part of the newly formed CDC-funded national network of centers
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, telecommunications or related field. Other requirements include: Strong background in wireless communication systems and networks Expertise in physical layer and MAC layer design for wireless systems Proven track
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757