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Networks. Preferred Qualifications: ● Experience in quantum network experiments/devices. ● Experience in writing research proposals. ● Experience in supervising/guiding PhD and/or MS students
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in network science. NetSI faculty and researchers are working to advance the understanding of complex systems through the lens of networks, and to apply this knowledge to solve real-world problems in a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
to local communities and corporations – we turn complex, large-scale, and often messy datasets into actionable insights that shape policy, drive accountability, and create real-world impact. We operate with
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Postdoc (f/m/d) in Irradiation Tolerance of Additive Manufactured Ferritic/Martensitic Steels for...
. Ferritic/martensitic steels, especially the Eurofer alloy specifically developed for fusion applications, are among the most promising material candidates. To enable the fabrication of complex geometries and
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Networks. Preferred Qualifications: ● Experience in quantum network experiments/devices. ● Experience in writing research proposals. ● Experience in supervising/guiding PhD and/or MS students
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University of North Carolina at Greensboro | Greensboro, Indiana | United States | about 23 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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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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. Key qualifications include: Proven ability to develop and apply complex in vitro or ex vivo test systems. Hands-on experience with microbial assays, biofilm models, and/or biomaterial characterization
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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in model-based reinforcement learning to join the Computer Science and Networks (INFRES
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-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging