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Field
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To combat climate change and achieve the UK's target of Net Zero, it is expected that the integration of renewable energy sources (RESs) at the distribution/consumption level will keep increasing
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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on recent advances in recombinant RNAP production, cryo-EM structural elucidation, and fragment-based screening, the project will integrate fluorine-based NMR spectroscopy with active learning algorithms and
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help
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development. This entitlement, from the Concordat to Support the Career Development of Researchers , applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP
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using cutting-edge computational techniques, including machine learning algorithms. Work collaboratively with an interdisciplinary and international team to refine and validate regional wave and ocean
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. The ML will use 500,000 fundus images from open-source and customised retinopathy datasets. We will compare retinopathy grading accuracy by NHS clinician vs ML algorithm. This will build on Exeter’s
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined