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Field
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aerospace, healthcare, and industrial automation. In safety-critical domains, such as aviation and medical devices, rigorous certification processes and continuous lifecycle monitoring are essential to ensure
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
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Supervisors Primary Supervisor - Dr Calum Williams Secondary Supervisors - Dr. Maciej Dabrowski , Prof Simon Horsley This PhD studentship will develop 3D-printed optical metamaterials to control
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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
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be undertaken at Imperial College London (Control and Power Research Group, Department of Electrical and Electronic Engineering) under the co-supervision of Professor Balarko Chaudhuri and Professor
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. This project will build upon our research and industrial successes, focusing on developing control solutions for automated robotic systems that can be teleoperated using intuitive human-machine interfaces
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improve surgical workflow, shortens surgery time, enables unrestricted movement tracking, and reduces infection risks. Eliminating markers enables robot-assisted or fully automated femoral implantation
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that experiments have historically been conducted without precise control over the environmental conditions, allowing too many variables to influence tree growth simultaneously. For decades, the field
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discovery that in contrast to mRNAs, nc transcripts rely on distinct and poorly understood mechanisms that control their RNA polymerase II (Pol II) transcription. As a result, ncRNAs are non-polyadenylated