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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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, especially LIDAR, radar and others. Tracking is also part of this task since it require monitoring the areas of interest and autonomous decision making. Possible applications to this research are: Unmanned
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High speed photography to investigate surface wear and fatigue in railway rail and wheel steels
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High speed photography to investigate surface wear and fatigue in railway rail and wheel steels School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof D Fletcher
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the ground, in different weather conditions. 4) develop methods for sensor management and data fusion linked with inference and decision making, jointly applied to several wildfire detection tasks. 5) embed
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to secure funding from external bodies as co-investigator, and knowledge of NIHR and other funding streams. Application & interview 9 Track record of contributing to and co-authoring high quality, peer
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lead a team to coordinate and report on delivery performance across the ITS product portfolio (tracking to metrics, including budget, resource, timeline and benefit realisation). You will be responsible
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). The group has a track record of providing novel insights into helicase function and structure-based drug discovery (https://doi.org/10.1093/nar/gkae897). We take a multi-disciplinary approach and collaborate