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Field
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the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational
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implementing innovative signal processing algorithms for radar sensing and communication, establishing clear research objectives, and engaging in the collaborative projects. In addition, the position involves
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Role Description A research position is available to support research to enhance observations of human exposure to air pollution and other pollutants using sensor-based approaches. Air pollution is
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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vehicle (AV), allowing for automated detection, prediction, mapping, and planning. During the vehicle’s operation, data is obtained through a myriad of sensors in an AV—including RADAR, LIDAR, cameras, and
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benefit society by advancing technologies that will underpin the design of the next generation of transport aircraft. Specifically, you will develop, test, and industrialise algorithms designed to simulate
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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us to build/learn generative, probabilistic forward models of users and their physical and computational environments. This will involve modelling sensors, developing dynamic models for control and
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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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, inaccessible to standard techniques. To probe such regimes requires the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated