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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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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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Bayesian system identification in nonlinear engineering dynamics
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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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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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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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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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these observations is that biases in the perceptual systems used by animals (including humans) to detect and process sensory information have played an important role in shaping communication signal evolution. In
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chatbots in other chronic conditions (e.g., Type 1 Diabetes Mellitus), this PhD project aims to develop the first developmentally appropriate chatbot for YP with IBD. Objectives: To gather the views of YP
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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