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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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of responses to images and model these representations with AI models (deep neural networks (including topographical), multimodal models, Large Language Models), 2) define and model dimensions related
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leverage deep neural networks to approximate value functions and policies, enabling scalability to high-dimensional state-action spaces. A key innovation will be the integration of distributed federated
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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling
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of diverse clinical scans and the anatomical structure of the heart. The model can be fine-tuned for different clinical tasks without retraining the entire network, enabling an agile workflow for performing
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the University of Sheffield and with industry partners, you will develop and optimise the modelling techniques. This includes the development of Physics Informed Neural Networks and combine this with real-time
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Neural mechanisms underlying accent processing in naturalistic conversations: Insights from brain oscillations and functional neuroimaging Prof. Guillaume Thierry Applications are invited for a
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mission. You will: Help collate data resources relevant to suicide and self-harm. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and
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private. This PhD will focus on three strands of work: 1) Innovate NILM model structures. Design efficient neural network architectures for both aggregator and client models that meet strict accuracy
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.” Research will contribute to the creation of new approaches and standards for an intelligent networks and agentic web with particular emphasis on policy-based authorization, delegation of entitlements, and