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programme in probabilistic AI. The hub will develop the next generation of mathematically principled, scalable and uncertainty-aware AI algorithms. This will be achieved through: bringing together world
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience against different interfering systems. Develop, with colleagues, a spectrum
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Bay. The key responsibilities of this role include; Using a combination of automated algorithms and manual data processing to identify bottlenose dolphin signature whistles in a multi-year acoustic
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high