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optimisation techniques and AI-based models to support decision-making in microgrid design and operation. Working in collaboration with a leading global mission critical firm of engineers, this project offers
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. • To develop and improve theoretical models of fluid dynamics, solid mechanics, soft matter or active matter. • To carry out analytical calculations in the context of theoretical models of fluid
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; · Physical layer cybersecurity for reconfigurable intelligent surface and fibre network; · Advanced energy storage solutions and integration into the smart grid ecosystem. · Applied AI techniques
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the Department of Engineering, with a focus on digital twin modelling for marine engines and propulsion systems involving different decarbonisation technologies, as well as the development of AI-driven decision
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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; supervising student projects/practicals. · Demonstrable ability to model experiments using a range of software packages, such as Python, Matlab or Mathematica. · Experience of experimental
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investigate the “Solar wind outflow and open flux”. Candidates with a strong track record in solar magnetic field modelling, and/or familiarity with solar observational data, are particularly encouraged
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• Experience with super conducting microwave circuit design of fabrication • Experience of materials and/or device modelling software • Knowledge of Labview and/or Python software. How
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healthy life. Similarities in the structural and molecular organization of the division apparatus in a variety of model systems give the impression that the mechanisms underlying division are shared between
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high