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Field
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(e.g., transportation networks, manufacturing systems, and truck routing). Assessing the relevance of the intake fraction (i.e., exposure efficiency) of major emission sources as a critical metric for
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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(e.g., transportation networks, manufacturing systems, and truck routing). Assessing the relevance of the intake fraction (i.e., exposure efficiency) of major emission sources as a critical metric for
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processing, information and communication theory, machine learning, complex networks, and optimization. The employment This employment is a temporary contract of two years with the possibility of extension up
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 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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to grow by 20% by 2030, these challenges require a shift from isolated logistics operations to collaborative, connected logistics networks. Upcoming policy measures, including kilometre chargers, CO2 caps
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, seven European universities from the Coimbra network, and partner universities from across the globe to explore further how we can effectively decolonise university heritage. Through analyses
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road conditions. Your specific activities will include (but are not limited to): • Develop robust, production-grade machine learning solutions for predictive modelling and complex decision
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infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active
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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine