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Field
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-critical decisions in real time. These systems rely heavily on sensor data (e.g., GPS, pressure transducers, image processors), making them vulnerable to stealthy threats like False Data Injection (FDI) and
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, or physics, and will demonstrate a willingness for experimental fieldwork and data analysis. This PhD offers a unique opportunity to contribute to frontier research in atmospheric chemistry, with
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conducting the qualitative interviews with patients and analysing the interview data. You will be presenting that data to the study team at regular meetings and contributing to the intervention and app
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Are you passionate about artificial intelligence, data analytics, and cutting-edge engineering applications? Join our interdisciplinary research team and contribute to the future of Non-Destructive
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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Applications are invited for a PhD studentship in the Department of Computer Science at City, University of London. The successful candidate will work on developing a novel AI-powered conversational
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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions
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Department/Location: Department of Engineering, Central Cambridge A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on experimental investigations
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, and materials science, with a strong publication record (h-index 36, i10-index 69). The second supervisor is Dr. Indrat Aria, a materials scientist with expertise in low-dimensional nanomaterials and
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factors (learning curve). These will be linked with the CCTA imaging biomarker data (quantitative plaque analysis) that is being collected as part of the EU HORIZON TARGET study to determine personalised