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(BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology. Develop in-situ and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 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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University of North Carolina at Greensboro | Greensboro, Indiana | United States | about 3 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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. 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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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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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
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who has been working in an academic or commercial research laboratory/company related to the differentiation of human iPSC into different cell lineages, and the assembly of complex tissue culture models
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or (iv) develop new representations of phytoplankton physiology/productivity. The post will be based around using and/or developing the ocean model PISCES, which is a complex model representing the cycling
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this, the Fellow will implement a universal design methodology for such fluids of complex rheology, using a Machine Learning (ML) algorithm to be incorporated in a Computational Fluid Dynamics framework. Training