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immobilisation, or even the chemical-physical environments thought to underpin the origin of life. This project offers a unique opportunity to work at the interface of experimental physics, data science, and
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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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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker
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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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-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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fit if you: Have a background in Biochemistry, biomedical sciences, organic chemistry and biophysics Are desire to explore the biochemical composition and biological activity of GAGs Are confident
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. The Faculty of Population Health Sciences has established itself as UCL’s largest research-based faculty, encompassing eight institutes. More information available at www.ucl.ac.uk/population-health-sciences
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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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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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, 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