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. The ideal candidate for the position is a driven early-career scientist with a deep and genuine interest in seeking to better influence and complement chemical synthesis by inferring mechanistic details
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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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