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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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epidemiology and machine learning. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional support for research expenses
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. Using gastruloids as a model system with which to study GAG structure/function relationships. Generating gastruloids from induced pluripotent stem cells (iPSCs) to create in vitro models for studying
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter