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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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behaviour of so-called yield stress fluids, which keep their shape like solids at low loads, yet flow like a liquid at larger loads. One possible focus could be on the dynamical process whereby a material in
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gases, including the operation of magneto-optical traps, the production of quantum degenerate gases and optical trapping using optical tweezers/lattices. They will be expected to display initiative and
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tools that enable optimal design and operation of greener vessels, backed by real-world demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI
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aim to provide a supportive and friendly environment with a strong sense of community. The Department currently has 116 permanent members of staff, following a process of significant growth
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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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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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modelling of shapes changing in time, and involves mathematical and statistical techniques from statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics
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Confident Leader status. If you are a candidate with a disability, we are committed to ensuring fair treatment throughout the recruitment process. We will make adjustments to support the interview process
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gases, including the operation of magneto-optical traps, the production of quantum degenerate gases and optical trapping using optical tweezers/lattices. They will be expected to display initiative and