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quality modelling approaches to analyse the flow interactions between sewer discharge of surface runoff, as well as the dynamics of pollutants and pathogen propagations associated with water movements. The
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Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management
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. Mentor students in research-related activities and offer guidance to PhD candidates within the discipline when necessary. Contribute to the development of novel models, techniques, and methodologies
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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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data. Apply knowledge in a way which develops new intellectual understanding. Contribute to developing new models, techniques and methods as required by their research project. Present research outputs
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Job no:520746 Work type:Full Time Location:Crawley Categories:Engineering and Mathematical Sciences, Social Sciences Lead numerical modelling of carbonate sand for offshore foundations, support
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sections, incorporating uncertainty quantification into AI/ML models and providing useful information in aid of UQ in AI/ML models to discern patterns in physical properties. The position will be highly
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methods for monopiles and validating models through centrifuge testing. Contribute to research dissemination through publications, conferences, and supervision of students. About you PhD or equivalent
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research team. Lead and support data analysis by conducting and interpreting advanced statistical analyses, including latent growth mixture modeling, and contribute to the integration of quantitative and
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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity