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Field
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to understand and predict how technologies evolve — from artificial intelligence to net-zero innovations in energy, transport, and carbon capture. By building a global database on technological progress, we seek
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architecture and design for complex socio-technical systems Graph theory, network science, and knowledge representation Agent-based and simulation modeling AI/ML, foundation models, causal inference, and
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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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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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: 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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. 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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nucleation and organisation in order to generate specialised microtubule arrays, such as the mitotic spindle or the polarised microtubule networks within neurons. We focus on gamma-tubulin ring complexes (g
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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
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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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). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge of interpretability methods to ensure ML outputs are meaningful in scientific