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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis
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Communication, Singal Processing, Low Power Electronics, Wireless Sensing, Low-Power System Design, Machine Learning & Edge Inference, Underwater acoustic communication. Furthermore, you have a proven record of
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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). Footage will be collected using various underwater cameras deployed near the seabed. The sampling methodology is considered non-invasive because no fish is harmed, and the sampling infers almost
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across the value chain. Using Bayesian Optimization / Modern Design of Experiment, we build the data-foundation to enable true hybrid development between humans and advanced learning algorithms such as
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to survive transients without exceeding load limits; ▫ maximizing cut-out wind speed(s); Key aspects include observing & inferring usable flow field quantities via the AWES device; trajectory optimization