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modelling and analytical skills in building and testing an acoustic monitoring tool to detect hidden defects in sewer pipes as a part of the EU multi-institutional project AI:LINERS. The work will be carried
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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline
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year round Details This research is aimed at developing scalable Bayesian approaches able to solve complex and high dimensional problems with multiple objects and multi-sensor data. One such problem is
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Bayesian system identification in nonlinear engineering dynamics
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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which videos can be with low resolution and low quality. Based on the video data coming sequentially in real time the most common problems of interest are: automatic detection of moving objects, followed
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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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Human Grip of Hand-Held Objects School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof M Carre, Prof R Lewis Application Deadline: Applications accepted all
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detect changes in step quality associated with acquired brain injury?” and involves a programme of laboratory-based work using a smart pressure-sensor based insole and app. We aim to develop biomechanical
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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