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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative
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Supervisor: Professor Richard Gilbertson and Dr Giulia Biffi For further information about the research group, please visit biffilab.wordpress.com . Project details Cancer-associated fibroblasts
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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of novel AM materials on corrosion response of key component and develop a model to predict their behaviour. To address the goals set for tackling international climate change, the power sector needs
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mortality using traditional and new forms of data, with a focus on developing and low-income countries. The successful applicant will spend 18 months at LSHTM and enrol in the PhD programme, with fees funded
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during the application period. For more information on the position, please contact principal investigators; Prof. Hamid Reza Godini and Prof. Riikka Puurunen . For more information on the process, please
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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