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necessitates a tenfold increase in bit rates, enhanced user capacity, and significantly reduced power consumption. Given that 80% of data traffic is directed indoors, the current reliance on outdoor base
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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architecture search to generate a family of related models. An AI service can then trade accuracy for lower energy and carbon at runtime, picking the right model for the current grid status. You will be
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quantify the impact of nonlinearity on flow and transportin confined porous media. We put particular emphasis on three sources of nonlinearity: non-Newtonian rheology, large driving rates leading to dynamic
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systematic evidence on where it adds value and where its risks lie, is lacking. In this PhD project you will map current and future GenAI use across the full HTA lifecycle, from horizon scanning
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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, chemotherapy, viral infections and diabetes mellitus. Looking at clinical studies, the current research process is to propose a model, conduct animal tests and then evaluate whether the original model is
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wind–wave flume. Measurements obtained from these controlled experiments will be used to validate and refine the numerical models. The validated models will then be used to identify critical wave
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probabilistic and deterministic reliability criteria with market operation in a single security-constrained optimal power flow model, and combining optimal power flow analysis with reliability analysis methods
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PhD: Mixing in turbidity currents in experiments and submarine canyons Faculty: Faculty of Geosciences Department: Department of Earth Sciences Hours per week: 36 to 40 Application deadline: 2