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of Biomedical Engineering & Imaging Sciences at King's College London. You will become part of a multidisciplinary team of physicists, computer scientists, AI engineers, radiochemists, and biologists, with access
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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of bioelectrochemical systems, electrochemical engineering, resource recovery and sustainable battery recycling. The project aims to translate a laboratory-validated lithium recovery process into a continuous-flow
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of bioelectrochemical systems, electrochemical engineering, resource recovery and sustainable battery recycling. The project aims to translate a laboratory-validated lithium recovery process into a continuous-flow
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that will be collected. You will also have the chance to contribute to tackling unique image processing challenges in whole-brain 3D light-sheet microscopy data. The successful applicant will be a member of
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that will be collected. You will also have the chance to contribute to tackling unique image processing challenges in whole-brain 3D light-sheet microscopy data. The successful applicant will be a member of
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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cardiorespiratory CT imaging by seeking a year’s clinical research experience, allowing them time to write grants and apply for further fellowships leading to a higher degree (such as an MD or PhD) or use
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across the Schools of Pharmacy and Medicine. We have recently used magnetic resonance imaging (MRI) technique to image what happens in the bowel to tablets and capsules that people swallow to take their
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a 8 fs laser setup and generate structural light from it. You will work collaboratively within the team at Imperial College. You will hold a PhD in Physics (or a closely related quantitative