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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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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically focused research group that maintains regular interaction
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, the project will develop algorithms for ecological sensing, adaptive motion planning, and energy optimisation under real-world constraints. Scaled experiments and high-fidelity simulations will validate system
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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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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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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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. 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
: Algorithm Validation and Use Case Demonstration (Months 27–36): This WP will first develop an integrated hardware–software testbed to systematically validate the performance of proposed solutions under