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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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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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can do mechanical work, long a dream of science fiction, for instance for implantable biodevices in healthcare, chemical remediation, or low cost sensors. One promising direction is to integrate
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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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, curriculum over geometry/BCs, calibration of predictive uncertainty, and robustness to sensor noise. Tasks: forward prediction (temperature fields), inverse reconstruction (defect size, depth, orientation
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. This PhD project will investigate the interactions between wildfire disturbance and thermokarst dynamics across Siberia and other Arctic regions using multi-sensor satellite remote sensing data provided by
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, stable and radiocarbon isotopes, pyrogenic carbon, microclimate) and develop a new soil microcosm experiment. Training will include tropical field methods, environmental sensors, experimental design 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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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