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Field
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: Background in signal processing, network science, or statistical physics applied to time series and/or complex systems analysis. Familiarity with PNT data, spatiotemporal datasets, or related domains
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the study of the development of singularities, the long-time behavior of complex systems, the formation of spatial patterns, the transition between stability and instability, and the first steps of transition
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. The work will use microorganisms as a starting base, as these are biologically relevant, yet complex and challenging to resolve spatially, provide an ideal benchmark for the method’s performance
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Postdoctoral Researcher in Synthetic Organic Chemistry and Photochemistry, IBMM, Montpellier, France
tissues. 3D printing technologies have boosted progress in the field by enabling the fabrication of complex engineered tissues. Nevertheless, printed scaffolds cannot faithfully recapitulate the spatial
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
data inputs and linear models. Consequently, they struggle to capture the complex, non-linear interactions between fuel conditions, topography, weather dynamics, and anthropogenic activity
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. This agent will be designed to integrate and interpret complex patient data using tools such as neural networks for analyzing patient imaging data; language models for analyzing patients' clinical and lab
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relevant, yet complex and challenging to resolve spatially, provide an ideal benchmark for the method’s performance. Microorganisms such as bacteria, furthermore, play important roles in all types of niches
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing
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. Proficiency in Python programming and major ML/DL frameworks (e.g., PyTorch, TensorFlow). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge