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Field
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Ultrafast lasers drive innovations from quantum technology to medical imaging, yet controlling femtosecond pulses remains a major challenge. Metamaterials are artificial structures with
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and therefore, the ability of a liquid fuel structure to resist deformation by the air stream. This is of particular relevance to gas turbine fuel injectors which are operated at high pressure and
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materials interact with the body. This project addresses that gap by engineering a 3D-printed full-thickness skin model that mimics the aging microenvironment, enabling more predictive evaluation of novel
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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Computational Fluid Dynamics (CFD) to diagnose the air quality status of those spaces (presence of pollutants, ventilation, humidity) and to propose measures to improve it. Such measures might imply retrofitting
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the Department of Biomedical Engineering at Swansea University, but you will also interact closely with our national network of clinicians from across the UK. This ensures the project stays grounded in clinical
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aims to develop a novel theoretical framework for nonlinear and robust control of dynamical systems from a phase perspective. You will have the opportunity to freely explore multiple research directions
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not fully capture the high-temperature, complex thermal-fluid interactions within the pebble-bed. This PhD project will focus on advancing porous media models for pebble-bed HTGRs by leveraging newly
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providing a structured, semantic framework that enhances knowledge sharing and data reuse across different platforms and systems. Project Aim This PhD will develop an ontology-based methodology to improve
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will dynamically adjust turbine parameters such as yaw, pitch, and torque to maximize Annual Energy Production (AEP) while minimizing component stress. Additionally, a hybrid predictive maintenance model